case study of health care in iot

IoT in Healthcare: Applications and Use Cases

5G Is Here Learn what it means for your enterprise Download PDF The growth of IoT into nearly every business arena from medical devices and healthcare applications to industrial IoT (IIoT) is amazing to behold. Our series highlighting the range of use cases for the Internet of Things illustrates how IoT products and services are being deployed around the globe, by industry. This article focuses on the range of IoT use cases in healthcare today, supporting patients, doctors, medical staff and first responders in achieving better outcomes. Why is IoT in healthcare a fast-growth industry? There are a number of reasons, including the capability of connected devices to monitor health vitals, route data, provide alerts, administer medications and automate critical processes. The medical industry is adopting Internet of Things technologies in everything from medical wearables to patient monitoring and pharmaceutical temperature monitoring in order to improve accuracy, promote efficiency, reduce costs, meet compliance requirements and enhance health and safety. In fact the term "healthcare IoT" or HIoT has been coined to describe this market niche. Digi solutions support development and deployment of a broad range of products and applications in this space.

Let's take a tour of some examples of IoT in medical and healthcare, including Digi customer case studies that help to demonstrate the breadth of IoT applications in healthcare patient support. You can find more examples of applications for a range of industries in the  Customer Stories  section of the Digi site.    

IoT in Healthcare - Promoting Hygienic Hospitals and Clinics

• Contact tracing
• Pathogen detection
• Thermal detection (elevated temperature)
• No-touch sanitation dispensers
• Automated hand hygiene 
• Hygiene monitoring
• Workspace and floor sanitation
• Air quality sensors
• Biometrics scanners
• Vital signs monitoring
• Remote patient communications
• Instrument sterilization
• Medication dispensing

Here are a few examples of how Digi customers have built healthcare applications supporting sanitation and hygiene.  

Floorbotics

Clean Hands Safe Hands

Get to Market Faster and Avoid Costly Mistakes: New FDA Guidance for RF Wireless Medical Devices

BOS Technology

IoT Wearables: Health Monitoring, Injury Reduction and Contact Tracing

With advances in Bluetooth technology, and the need for immediacy in feedback, wearables are an enormous growth area for IoT in healthcare. In this section, meet some of the Digi customers who are designing wearables for wellness, ergonomics, contact tracing and patient/doctor connectivity.  

Kinetic Wearables

Kinetic Updates for Contact Tracing

Additional worker safety concerns surfaced with the Covid-19 pandemic, especially in the close environments of industrial workplaces. Reducing worker virus transmission and quickly and accurately identifying potential risks have become key priorities in keeping industrial employees safe and operations open. “When Covid infections among industrial workers began forcing facility shutdowns, we saw a need to better leverage smart technology to connect essential employees and help protect them from illness,” said Bansal.   

Related content:  Watch the Kinetic video

Lasarrus wearables for physical therapy.

"In the era of COVID-19, fewer patients or clinicians want to have in-person patient encounters," said co-founder Nelson Emokpae. "We’re recognizing that the LASARRUS WearME can play an important role in fighting the pandemic from a telehealth perspective. First, patients can wear our device from home and enable the clinician to quickly obtain a complete physiological assessment. That will improve patient outcomes without exposing them to unnecessary risk."

Download our Reliable Connected Health Brochure

Learn how industry-leading Digi solutions are purpose-built for today’s connected medical devices.

Download PDF

IoT in Patient Care and Pain Medication Management

Related content: Learn about the secure, scalable Digi ConnectCore 8 module family

Medical iot: 3d imaging technology.

Pharmaceutical Temperature Monitoring and Compliance

IoT Use Cases in Emergency Response and Critical Communications

Related white paper: Mission Critical Communications for Traffic Management

Iot in telemedicine, remote surgery, robotics.

Related blog post:  How 5G Will Impact the Future of Healthcare

  More IoT Applications and Use Cases In addition to IoT use cases in healthcare, you can find more IoT use cases and examples in these blog posts:

• IoT Applications for Retail
• IoT Applications in Smart Cities
• IoT Solutions for Transportation
• IoT Applications for Precision Agriculture
• Digital Signage IoT Applications
• Connected Vehicle Use Cases
• IoT Applications in Supply Chain
• IoT Drones: How the Use Cases are Changing

 How to Connect with Digi:  

• Contact us to start a conversation about your IoT development or deployment plans.
• Sign up for our newsletter to keep up with the latest content and new product introductions.
• Connect with us on LinkedIn , Twitter , or Facebook .

Note: This post was initially published in February of 2019, and was updated in July of 2021.

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IoT Case Studies in Healthcare

Discover the latest case studies in healthcare and explore how the healthcare industry is leveraging the Internet of Things (IoT) to enhance patient care and streamline operations. Our case studies showcase real-world examples of businesses using IoT to monitor patient health, improve medical equipment maintenance, and enhance patient experiences. With IoT, medical providers, such as pharmaceutical manufacturers, can improve efficiency, reduce costs, and ultimately improve patient outcomes.

Browse through our selection of IoT cases to see how innovative technologies are transforming the medical and healthcare industry and improving the lives of patients and providers alike. Or why not explore IoT case studies from other industries?

8 West: From Silver Surfers to Search and Rescue, Connected Wearables Put Safety First

The danish heart association: digital monitoring of defibrillators, cardilink: delivering lifesaving connectivity when it’s needed most, m2cloud: creating the next generation of pharmaceutical supply chains in south korea, amber: weathering the coronavirus storm in the caribbean, customer success stories, more iot cases and examples, 15 iot examples for business applications, get a free consultation.

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IoT Revolutionizing Healthcare: A Survey of Smart Healthcare System Architectures

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IoT-Based Secure Health Care: Challenges, Requirements and Case Study

• First Online: 19 July 2022

Cite this chapter

• Sohail Saif 8 ,
• Pratik Bhattacharjee 9 ,
• Koushik Karmakar 10 ,
• Ramesh Saha 11 &
• Suparna Biswas 8  

Part of the book series: Smart Computing and Intelligence ((SMCOMINT))

316 Accesses

Delivering health care to people has become revolutionizing due to the technological advancement of embedded systems and medical devices. Integration of different health sensors, handheld devices and the Internet can be a great potential for significant improvement of the quality of remote health care. Physiological sensor devices used for monitoring vital signs are gaining popularity. This network of sensor and coordinator devices has led to the growth of Body Sensor Network (BSN). Since patient health information is transmitted through this Body Sensor Network and stored in medical servers, these data are vulnerable to security threats. Security attacks on communication channels or malware in the sensor devices can lead to incorrect data collection by devices which can result in wrong diagnosis and treatment. So the security of health data is of utmost concern which needs to be handled carefully. This chapter intends to discuss recent advancements in security mechanisms to secure health data. This chapter can help the readers to get an overview of secure health care by discussing the traditional architecture, various security attacks on healthcare data, security requirements, available solutions and case studies. Open research issues in this field are also discussed which can motivate the researchers in this field.

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Adeleke, O. (2020). Intrusion detection: Issues, problems and solutions. In: 2020 3rd International Conference on Information and Computer Technologies (ICICT) . IEEE, pp. 397–402.

Google Scholar  

Alam, M. M., Malik, H., Khan, M. I., Pardy, T., Kuusik, A., & Le Moullec, Y. (2018). A Survey on the roles of communication technologies in IoT-based personalized healthcare applications. IEEE Access, 6 , 36611–36631.

Article   Google Scholar  

Almuhaideb, A. M., & Alqudaihi, K. S. (2020). A lightweight and secure anonymity preserving protocol for WBAN. IEEE Access, 8 , 178183–178194.

Annas, G. J. (2003). HIPAA regulations—a new era of medical-record privacy? New England Journal of Medicine, Massachusetts Medical Society, 348 , 1486–1490.

Banyal, R. K, Jain, V. K, & Jain, P. (2014). Dynamic trust based access control framework for securingmulti-cloud environment. In: Proceedings of the 2014 international conference on information andcommunication technology for competitive strategies, ICTCS 14 . ACM, New York, pp 29:1–29:8.

Begli, M., Derakhshan, F., & Karimipour, H. (2019). A layered intrusion detection system for critical infrastructure using machine learning. In: 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE) . Oshawa, ON, Canada, pp. 120–124.

Bhattasali, T., Chaki, R., Chaki, N., & Saeed, K. (2018). An adaptation of context and trust aware workfloworiented access control for remote healthcare. International Journal of Software Engineering and Knowledge Engineering, 28 (06), 781–810.

Chakrabarty, B., Kothekar, A., Mujumdar, P., Raut, S., Patil, S., & Ukirde, D. (2020). Securing data on threat detection using IBM spectrum scale and IBM QRadar.

Chang, S. Y., Hu, Y. C., Anderson, H., Fu, T., & Huang, E. Y. L. (2012). Body area network security: Robust key establishment using human body channel. In: Proceedings of the USENIX Conference on Health Security and Privacy , pp. 5–5.

Dhanda, S. S., Singh, B., & Jindal, P. (2020). IoT security: A comprehensive view. In: Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm . Springer, Cham, pp. 467–494.

Finding Advanced Threats Before They Strike: A Review of Damballa Failsafe Advanced Threat Protection and Containment. (2021). http://www.sans.org/readingroom/whitepapers/analyst/finding-advanced-threatsstrike-review-damballa-failsafe-advanced-threatprotecti-34705 . (Accessed 7 March 2021).

Gupta, A., Tripathi, M., & Sharma, A. (2020). A provably secure and efficient anonymous mutual authentication and key agreement protocol for wearable devices in WBAN. Computer Communications, 160 , 311–325.

Hasan, K., Biswas, K., Ahmed, K., Nafi, N. S., & Islam, M. S. (2019). A comprehensive review of wireless body area network. Journal of Network and Computer Applications, 143 , 178–198.

He, D., & Zeadally, S. (2015). Authentication protocol for an ambient assisted living system. IEEE Communications Magazine, 53 (1), 71–77.

He, D., Qiao, Q., Gao, Y., Zheng, J., Chan, S., Li, J., & Guizani, N. (2019). Intrusion detection based on stacked autoencoder for connected healthcare systems. IEEE Network, 33 (6), 64–69.

Hu, C., Zhang, N., Li, H., Cheng, X., & Liao, X. (2013). Body area network security: A fuzzy attribute-based signcryption scheme. IEEE Journal on Selected Areas in Communication, 31 (9), 37–46.

Hu, C., Cheng, X., Zhang, F., Wu, D., Liao, X., & Chen, D. (2013). OPFKA: Secure and efficient ordered-physiological-feature-based key agreement for wireless body area network. In: 2013 Proceedings of IEEE Infocom , pp. 2274–2282.

Hussain, S., Ahmad, M. B., & Ghouri, S. S. U. (2021). Advance persistent threat—a systematic review of literature and meta-analysis of threat vectors. Advances in Computer, Communication and Computational Sciences , 161–178.

Jabeen, T., Ashraf, H., Khatoon, A., Band, S. S., & Mosavi, A. (2020). A lightweight genetic based algorithm for data security in wireless body area networks. IEEE Access, 8 , 183460–183469.

Kaps, J. -P., & Sunar, B. (2006). Energy comparison of AES and SHA-1forUbiquitous computing. In: Proceedings of Emerging Directions Inembedded and Ubiquitous Computing (EUC 2006 Workshops) , pp. 372–381.

Karmakar, K., Saif, S., Biswas, S., & Neogy, S. (2018). WBAN Security: study and implementation of a biological key based framework. In: Fifth International Conference on Emerging Applications of Information Technology (EAIT) , Kolkata, pp. 1–6.

Khan, M. K., & Kumari, S. (2013). An authentication scheme for secure access to healthcare services. Journal of Medical Systems, 37 , 9954.

Kompara, M., Islam, S. H., & Hölbl, M. (2019). A robust and efficient mutual authentication and key agreement scheme with untraceability for WBANs. Computer Networks, 148 , 196–213.

Konan, M., & Wang, W. (2019). A secure mutual batch authentication scheme for patient data privacy preserving in WBAN. Sensors, 19 , 1608.

Kore, A., & Patil, S. (2020). IC-MADS: IoT enabled cross layer man-in-middle attack detection system for smart healthcare application. Wireless Personal Communications, 113 , 727–746.

Kumar, V., Jangirala, S., & Ahmad, M. (2018). An efficient mutual authentication framework for healthcare system in cloud computing. Journal of Medical Systems, 42 , 142.

Li, M., Yu, S., Lou, W., & Ren K. (2010). Group device pairing based secure sensor association andkey management for body area networks. In: Proceedings of IEEE INFOCOM , pp. 1–9.

Li, M., Yu, S., Guttm, J. D., Lou, W., & Ren, K. (2013). Secure ad-hoc trust initialization and key management in wireless body area networks. ACM Transactions on Sensor Networks, 9 (2).

Liang, X., Li, X., Shen, Q., Lu, R., Lin, X., Shen, X., & Zhuang, W. (2012). Exploring prediction to enable secure and reliable routing in wireless body area network. In: 2012 Proceedings IEEE Infocom , pp. 388–396.

Maitra, T., & Roy, S. (2017). SecPMS: An efficient and secure communication protocol for continuous patient monitoring system using body sensors. In: Proceedings of the 9th International Conference on Communication Systems and Networks (COMSNETS) , Bangalore, India, pp. 322–329.

Mana, M., Feham, M., & Bensaber, B. A. (2011). Trust key management scheme for wireless body area network. International Journal of Network Security, 12 (2), 75–83.

Mitchell, R., & Chen, I. (2015). Behavior rule specification-based intrusion detection for safety critical medical cyber physical systems. IEEE Transactions on Dependable and Secure Computing, 12 (1), 16–30.

Mitchell, R., & Chen, I. (2012). Behavior rule based intrusion detection for supporting secure medical cyber physical systems. In: 2012 21st International Conference on Computer Communications and Networks (ICCCN) . Munich, pp. 1–7.

Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., & Jamalipour, A. (2014). Wireless body area networks: a survey. IEEE Communications Surveys & Tutorials, 16 (3), 1658–1686.

Mucchi, L., Jayousi, S., Martinelli, A., Caputo, S., & Marcocci, P. (2019). An overview of security threats, solutions and challenges in WBANs for healthcare. In: 13th International Symposium on Medical Information and Communication Technology (ISMICT) . Oslo, Norway, pp. 1–6.

Mutlag, A. A., Ghani, M. K. A., Arunkumar, N., Mohammed, M. A., & Mohd, O. (2019). Enabling technologies for fog computing in healthcare IoT systems, future gener. Computing Systems, 90 , 62–78.

Ness, R. B. (2007). Influence of the HIPAA privacy rule on health research. JAMA, American Medical Association (AMA), 298 , 2164.

Newaz, A. I., Sikder, A. K., Babun, L., & Uluagac, A. S. (2020) HEKA: A novel intrusion detection system for attacks to personal medical devices. In: IEEE Conference on Communications and Network Security (CNS) . Avignon, France, pp. 1–9.

Oberoi, D., Sou, W. Y., Lui, Y, Y., Fisher, R., Dinca, L., & Hancke, G. P. (2016). Wearable security: Key derivation for body area sensor networks based on host movement. In: Industrial Electronics (ISIE), 2016 IEEE 25th International Symposium on 8–10 June . IEEE.

Roy, M., Chowdhury, C., & Aslam, N. (2020). Security and privacy issues in wireless sensor and body area networks, In: Handbook of Computer Networks and Cyber Security: Principles and Paradigms . Springer, ISBN: 978–3–030–22276–5. https://doi.org/10.1007/978-3-030-22277-2 .

Roy, M., Chowdhury, C., Kundu, A., & Aslam, N. (2017). Secure lightweight routing (SLR) strategy for wireless body area networks. IEEE Ants, 2017 , 1–4.

Ruoslahti, H. (2020). Business continuity for critical infrastructure operators. Annals of Disaster Risk Sciences: ADRS, 3 (1).

Saif, S., & Biswas, S. (2019). Secure data transmission beyond tier 1 of medical body sensor network. In: M. Chakraborty, S. Chakrabarti, V. Balas, & J. Mandal (Eds.), Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing , Vol. 811. Springer.

Saha, R., Naskar, S., Biswas, S., et al. (2019). Performance evaluation of energy efficient routing with or without relay in medical body sensor network. Health Technology, 9 , 805–815.

Saif, S., & Biswas, S. (2020) On the implementation and performance evaluation of security algorithms for healthcare. In: S. Kundu, U. Acharya, C. De, & S. Mukherjee (Eds.), Proceedings of the 2nd International Conference on Communication, Devices and Computing . Lecture Notes in Electrical Engineering, Vol. 602, pp. 629–640.

Saif, S., Gupta, R., & Biswas, S. (2018a) Implementation of cloud-assisted secure data transmission in WBAN for healthcare monitoring. In: S. Bhattacharyya, N. Chaki, D. Konar, U. Chakraborty, & C. Singh (Eds.), Advanced Computational and Communication Paradigms. Advances in Intelligent Systems and Computing , Vol. 706. Springer.

Saif S., Gupta, R., & Biswas, S. (2018b). Implementation of cloud-assisted secure data transmission in wban for healthcare monitoring. In: S. Bhattacharyya, N. Chaki, D. Konar, U. Chakraborty, & C. Singh (Eds.), Advanced Computational and Communication Paradigms. Advances in Intelligent Systems and Computing , Vol. 706. Springer.

Santos, O., Kampanakis, P., & Woland, A. (2016). Cisco next-generation security solutions: All-in-one cisco ASA firepower services, NGIPS, and AMP. Cisco Press.

Seimis, G., Huang, L., Masse, F., Tsekoura, I., Ashouei, M., Catthoor, F., Huisken, J., Stuyt, J., Dolmans, G., Penders, J., & Groot, H. D. (2011). A lightweight security scheme for wireless body area networks: Design, energy evaluation and proposed microprocessor design. Journal of Medical System, 35 (5), 1289–1298. Springer.

Shen, J., Tan, H., Moh, S., Chubg, I., Kiu, Q., & Sun, X. (2015). Enhanced secure sensor association and key management in WBAN. Journals of Communications and Networks, 17 (5).

Shi, L., Li, M., Yu, S., & Yuan, J. (2013). BANA: Body area network authentication exploiting channel characteristics. IEEE Journals on Selected Areas in Communication, 31 (9), 1803–1816.

Singh, A., & Chatterjee, K. (2019a). Trust based access control model for securing electronic healthcare system. Journal of Ambient Intelligence and Humanized Computing, 10 , 4547–4565.

Singh, A., & Chatterjee, K. (2019b). ITrust: Identity and trust based access control model for healthcare system security. Multimedia Tools and Applications, 78 , 28309–28330.

Suarez, N., Callico, G. M., Sarmiento, R., Santana, O., & Abbo, A. A. (2009). Processor Customization for Software Implementation of the AES Algorithm for Wireless Sensor Networks . Patmos, pp. 326–335.

Thamilarasu, G., Odesile, A., & Hoang, A. (2020). An intrusion detection system for internet of things. IEEE Access, 8 , 181560–181576.

Thamilarasu, G. (2016). iDetect: An intelligent intrusion detection system for wireless body area networks. International Journal of Security and Networks, 11 (82). Inderscience Publishers.

Yuce, M. R. (2010). Implementation of wireless body area networks for healthcare systems. Sensors and Actuators A: Physical, 162 (1), 116–129.

Zeadally, S., Isaac, J. T., & Baig, Z. (2016). Security attacks and solutions in electronic health (e-health) systems. Journal of Medical Systems, 40 (12), 263.

Zhang, Y., Gravina, R., Lu, H., Villari, & M., Fortino, G. (2018). PEA: Parallel electrocardiogram-based authentication for smart healthcare systems. Journal of Network and Computer Applications, 117 , 10–16.

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Acknowledgements

This work has been carried out with a grant received from WBDST sanctioned research project on secure remote health care with project sanction no. 230(Sanc)/ST/P/S&T/6G-14/2018.

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Sohail Saif & Suparna Biswas

Department of Computational Science, Brainware University, Kolkata, West Bengal, India

Pratik Bhattacharjee

Department of Computer Science and Engineering, Narula Institute of Technology, Kolkata, West Bengal, India

Koushik Karmakar

Department of Information Technology, Gauhati University, GUIST, Guwahati, Assam, India

Ramesh Saha

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Chandreyee Chowdhury

School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

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Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan

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About this chapter

Saif, S., Bhattacharjee, P., Karmakar, K., Saha, R., Biswas, S. (2022). IoT-Based Secure Health Care: Challenges, Requirements and Case Study. In: Biswas, S., Chowdhury, C., Acharya, B., Liu, CM. (eds) Internet of Things Based Smart Healthcare. Smart Computing and Intelligence. Springer, Singapore. https://doi.org/10.1007/978-981-19-1408-9_15

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IoT in Healthcare: Benefits, Use Cases, Challenges, and Future

• IoT in Healthcare:...

Internet of Things for Healthcare

How iot works in healthcare, iot devices in healthcare, benefits of iot in healthcare., challenges of iot in healthcare., why the internet of medical things is the future of healthcare , future of iot in healthcare.

Healthcare IT solutions will be a greater priority among IoT service providers once the disruptions caused by the COVID-19 slow down. Although remote sensing medical devices have existed for over more than two decades, and telemedicine has already been around for a while, the underlying technology has evolved 100x through the years. No more doctor visits. Today we have an interconnected network of intelligent devices capable of making decisions, work as groups, and send information to the cloud — Internet of Things. 

In this article, we will take a closer look at Internet of Things (IoT) for Healthcare. 

• Internet of Things for Healthcare.

What exactly is IoT and why it is important in healthcare? 

• IoT in healthcare examples .
• IoT Devices in Healthcare.
• Future of IoT in Healthcare.

Let's get to business.

The market of IoT in healthcare is predicted to exceed $10 billion by 2024, according to a Brandessence market research . This growth forecast is also impacted by other important technologies. IoT is slowly getting traction and evolving alongside the new ultra-fast 5G mobile wireless, Artificial Intelligence (AI), and Big Data. Combing this powerful technologies with the Internet of Things will likely revolutionize the healthcare industry. IoT in healthcare using 5G wireless and AI could, for example, completely transform the way patients are monitored and treated remotely.

Still, IoT will not only help with the patient’s health, but also improve the productivity of the healthcare industry workers. 

In a nutshell, IoT is the concept created around the idea of full ubiquitous computing, which is the processing of information linked with external activity or objects. Ubiquitous computing involves connecting electronic devices with microprocessors and sensors to talk to each other. IoT is a ubiquitous network except that all of those electronic devices have access to the Internet.  

IoT in the healthcare industry is a great example of this omnipresent computing. For example, hundreds of intelligent electronic devices can be set up in a hospital to monitor patients’ health status 24/7, talk to each other, make decisions, and upload information to a healthcare cloud platform.

IoT in Healthcare Examples

How IoT can be used in healthcare effectively? Let’s explore three workable Internet of Things healthcare examples below. 

• Sensing and uploading up-to-date patient information to the cloud in emergency situations, from the ambulance or even from home. 
• Medical devices capable of performing self-maintenance. IoT healthcare devices will sense their own components, detect low thresholds, and communicate with medical personnel and manufacturers.  
• IoT and wearables can help home patients and elderly communicate directly with a healthcare facility.  
• Telemedicine can be considered a “primitive” form of an Internet of Things in healthcare example. With IoT, a patient can be observed and in some cases treated remotely through video cameras and other electronic actuators. 

To understand how the Internet of Things in Healthcare works, let’s see how IoT works in general. As discussed above, an IoT unit can be considered as a device with a sensor that can interact with the physical world and send information to the Internet. 

In healthcare, these devices can gather different patient data and receive inputs from health practitioners. An Internet of Things Healthcare example is continuous glucose monitoring for insulin pens that works effectively for patients with diabetes. 

All these devices are able to communicate with each other and in some cases take important actions that would provide timely help or even save a life. For example, an IoT healthcare device can make intelligent decisions like calling the healthcare facility if an elderly person has fallen down. After collecting passive data, an IoT healthcare device would send this critical information to the cloud so that doctors can act upon it — view the general patient status, see if calling an ambulance is necessary, what type of help is required, and so on. 

Thus, Internet of Things Healthcare can greatly improve not only a patient’s health and help in critical situations, but also the productivity of health employees and hospital workflows. 

How IoT helps in healthcare — Process 

Let’s explore an IoT healthcare workflow example:

• A sensor collects data from a patient or a doctor/nurse inputs data. 
• An IoT device analyzes the collected data with the help of AI-driven algorithms like machine learning (ML). 
• The device makes a decision whether to act or send the information to the cloud. 
• Doctors, health practitioners, or even robots are enabled to make actionable and informed decisions based on the data provided by the IoT device. 

Although not all IoT devices should have a sensor, they at least need to have a radio and a given TCP/IP address to enable communication with the Internet. As long as a device has access to the Internet, it can be considered an IoT device. 

So, every smartphone is an IoT device. A smartphone with the right set of healthcare apps can help you detect diseases and improve your health. Some examples of these are skin cancer detection apps that use your camera and AI-driven algorithms to map moles on your skin. Other examples would be sleep, yoga, fitness, and pill management apps. 

Still, smartphone is a smartphone. Monitoring healthcare is not its primary application. A dedicated healthcare IoT device can do significantly more.  

• Smartwatch. Wearables sold at consumer electronics stores come with a sensor and Internet connection. Some of them (like iWatch Series 4) can even monitor your heart rate, control diabetes, help in speech treatment, aid in improving posture, and detect seizures. 
• Insulin Pens and Smart CGM (Continuous Glucose Monitoring). These devices can monitor blood glucose levels and send the data to a dedicated smartphone app. Patients with diabetes can use these devices to track their glucose levels and even send this data to a healthcare facility. 
• Brain Swelling Sensors. These tiny sensors are implanted within the cranium to help brain surgeons keep track of severe brain injuries and avoid further deathly swelling. They measure pressure on the brain and are able to dissolve by itself in the body without further medical interference.
• Ingestible Sensors. Prescribed medication is swallowed with a tiny digestible medical sensor that sends a small signal to a wearable receiver on the patient, which, in turn, sends data to  a dedicated smartphone app. This sensor can help doctors ensure patients take their medication at all times. 
• Smart video pills. A smart pill can travel through a patient’s intestinal tract and take pictures as it travels. It can then send the collected information to a wearable device, which in turn would send it to a dedicated smartphone app (or straight to the app). Smart pills can also help visualize the gastrointestinal tract and colon remotely. 

IoT in the healthcare industry has countless benefits. However, the most important is that treatment outcomes can be significantly improved or maximized, as the data gathered by IoT healthcare devices is highly accurate, enabling informed decisions. 

Health facilities and practitioners will be capable of minimizing errors because all patient information can be measured quickly and sent to a board of doctors or a healthcare cloud platform. AI-driven algorithms running on these IoT devices could also help make intelligible decisions or suggestions based on existing data.

Another great benefit of IoT in healthcare is reduced costs. With IoT in healthcare , non-critical patients will be able to stay at home while various IoT devices monitor and send all important information to the health facility — meaning less hospital stays and doctor visits.

With detailed information received from lots of IoT devices, health facilities will also be able to improve their disease management. They’ll have more data in real-time coming in than ever before. Still, this entails a number of challenges.

Although IoT in healthcare provides many great benefits, there are also some challenges that need to be solved. The Internet of Things Healthcare solutions cannot be considered for implementation without acknowledging these challenges. 

• Massive inputs of generated data. Having thousands of devices in a single healthcare facility and a thousand more sending information from remote locations — all in real-time — will generate huge amounts of data. The data generated from IoT in healthcare will likely make storage requirements grow much higher, from Terabytes to Petabytes. If used properly, AI-driven algorithms and cloud can help make sense of and organize this data, but this approach needs time to mature. So, creating a large-scale IoT healthcare solution will take a lot of time and effort.
• IoT devices will increase the attack surface. IoT healthcare bring numerous benefits to the industry, but they also create numerous vulnerable security spots. Hackers could log into medical devices connected to the Internet and steal the information —  or even modify it. They can also take a step further and hack an entire hospital network, infecting the IoT devices with the infamous Ransomware virus. That means the hackers will hold patients and their heart-rate monitors, blood pressure readers, and brain scanners as hostages.
• Existing software infrastructure is obsolete. IT infrastructures in many hospitals are obsolete. They will not allow for proper integration of IoT devices. Therefore, healthcare facilities will need to revamp their IT processes and use new, more modern software. They will also need to take advantage of virtualization (technologies like SDN and NFV), and ultra-fast wireless and mobile networks like Advanced LTE or 5G. 

IoT in healthcare industry can improve components, such as medical gadgets or services. It can also enhance healthcare applications, such as telemedicine, patient monitoring, medication management, imaging, and overall workflows in hospitals.It can also create new ways of treating different diseases. 

The Internet of Things for healthcare will not only be used by hospitals or facilities, but also by surgical centers, research organizations, and even governmental institutions. 

IoT in healthcare industry does not stand alone. All IoT devices and their networks need to be combined with other technologies to help healthcare facilities transform in a meaningful way. As mentioned before, IoT will revolutionize the healthcare industry but it also needs data, high-speed communication, and proper security and compliance. 

5G will provide the ultra-low latency speeds and mobility that the IoT in the healthcare industry needs. In turn, AI-driven solutions will make sense of the data lakes gathered from a collection of devices. Big Data strategies will use such AI algorithms to analyze data in real-time and make critical health decisions. Virtualization will help to reduce or get rid of old infrastructure in hospitals. 

IoT along with medical ERP software help healthcare evolve, and this evolution will only continue. Sooner than later, healthcare and Internet of Things will become inseparable, completely transforming how we approach our healthcare.

We, at Intellectsoft, empower companies and their healthcare workforce with cutting-edge transformative solutions and data-driven insights. Are you and your organization ready to shift the mindsets and get the most out of innovations? Talk to our experts and find out more about the topic and how your business or project can start benefiting from it today!

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A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective

Hadi habibzadeh.

Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203

Karthik Dinesh

Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627

Omid Rajabi Shishvan

Andrew boggio-dandry, gaurav sharma, tolga soyata, associated data.

The algorithms described in the previous subsection require a significant amount of well-structured clinical data to achieve useful levels of accuracy. Providing such data presents multi-faceted challenges, specifically, with respect to widespread data availability and heterogeneity.

A major data-related challenge in clinical HIoT is assuring the generality of results. Most available pilot studies in the literature are developed and tested on small datasets (relative to the actual patient population size) [ 180 ]. Therefore, their applicability and efficacy in real-world scenarios remain uncertain; especially, considering that health and disease states can be highly correlated with many personal parameters such as gender, age, and ethnicity. This problem is even more acute for rare diseases. Consequently, data analytics approaches have the best chances for success if clinics and hospitals share their data with algorithm developers. The flow of such information is, however, fraught with unresolved legal and ethical complications. An example of this is the collaboration between Google’s DeepMind and the British healthcare provider, Royal Free, which shared its acute kidney injury dataset with DeepMind without acquiring patients’ explicit consent. The decision led to official scrutiny, which concluded that the deal was illegal [ 181 ], [ 182 ].

To take full advantage of the existing medical knowledge, it is common sense to use historical medical data as input to inference algorithms. Combined with the data that is being streamed from the HIoT platforms, this amalgamation of data can provide an effective data source for modern inference algorithms. However, historical data sources are diverse and highly unstructured as part of the data must be inferred from EHRs, insurance claims, medical textbooks, and scientific papers [ 183 ]. The language used in these documents can be vague, out of chronicle order, and incomplete. To cope with these challenges, NLP is often used. However, regardless of their remarkable progress in recent years, current NLP technologies are not efficient enough in these applications, even when supported by the resources of tech giants [ 180 ].

This data heterogeneity problem is exacerbated when we consider the fact that even the data obtained from the same HIoT platform can be highly-heterogeneous within itself. For example, the MC10 sensors used in our case study continuously evolve and additional clinically relevant attributes can be measured with every new generation of sensors, which effectively adds a new ”input dimension” to the existing data. Therefore, algorithms must be designed to cope with not only an increasing volume of data, but also an increasing variety.

In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

I. INTRODUCTION

A strong synergy exists between the technological advances in Internet of Things (IoT) and the emerging needs and directions of healthcare applications. With a rapid expansion in the deployment of IoT devices and increasing desire to make healthcare more cost-effective, personalized, and proactive, IoT is poised to play a strong role in all aspects of health management, and for our discussion, we refer to this important segment of IoT as the Healthcare Internet of Things (HIoT). HIoT can be broadly classified into two sub-categories: personal and clinical. Personal HIoT includes devices such as activity/heart rate trackers, smart clothes and smartwatches (e.g., Fitbit [ 1 ], Apple watch [ 2 ]) that are used by consumers for self-monitoring. These general purpose devices are not strictly regulated and are intended for use by consumers without involvement/guidance from physicians. Clinical HIoT devices are built specifically for health monitoring under the guidance —and with the involvement— of a physician. Examples include smart continuous glucose monitors [ 3 ] and connected inhalers [ 4 ]. These devices are intended for use in either clinical or home environments and are strictly regulated and approved for use only after clinical validation. This paper surveys the emerging field of clinical HIoT.

A confluence of social and technological trends is motivating the adoption of IoT in the clinical setting. On the social side, aging populations in the West are straining clinical institutions and healthcare costs are rising at significantly higher rates than baseline inflation [ 5 ]. On the technology side, most healthcare institutions are already connected to the internet and are, therefore, well poised to take advantage of the increasing availability of high bandwidth connectivity, inexpensive cloud storage and computation, and large-scale data analytics. HIoT technologies are attractive in this emerging scenario because they allow personalization of clinical healthcare enabling not only significant cost reductions but also improved outcomes through higher responsiveness, customization, and effective exploitation of aggregated data. HIoT can reduce the time taken to diagnose a health condition [ 6 ], provide efficient high-quality care, and help to reduce hospitalization costs [ 7 ] as well as the chance of readmission for the same health issue [ 8 ], [ 9 ]. Being connected with HIoT enables patients to provide continuous feedback to the doctors and monitor their own progress, which enhances patient engagement and satisfaction. Rich collections of longitudinal data from heterogeneous sources, made possible by HIoT adoption, also opens up new avenues for augmenting traditional diagnostic approaches employed by physicians. Specifically, data analytics can automatically flag physiological anomalies for further investigation and visualization technologies can summarize salient trends, without cognitively overloading physicians and interfering with their patient interactions in the clinic.

Several recent surveys address specific aspects of HIoT systems. Medical IoT devices focusing on personalized healthcare systems [ 10 ], and applications with toddlers/kids [ 11 ] have been reviewed. Applications of remote health monitoring systems such as body temperature monitoring and elderly care [ 12 ], and applications focusing on healthcare solutions using smartphones, ambient assisted living (AAL), and wearable devices have been studied in detail [ 13 ]. HIoT applications in rural healthcare focusing on improving healthcare in developing countries have also been reviewed [ 14 ]. Other survey papers include studies focusing on communication [ 15 ], security, and data privacy aspects of HIoT systems [ 16 ], [ 17 ]. HIoT in smart homes [ 18 ], high-risk environments, and safety industries [ 19 ], and HIoT based services for mental health [ 20 ] have also been the focus of several reviews. Distinct from these prior surveys, the present paper focuses particularly on —and provides end-to-end coverage for— the clinical side of HIoT. Additionally, the ideas discussed are concretely illustrated throughout the paper by using an example from the clinical setting, specifically from neurology.

The rest of the paper is organized as follows. Section II presents a discussion of trends, challenges, and application requirements that IoT adoption in the clinical settings faces. To motivate the subsequent discussions, example IoT-based health management applications are presented in Section III . This section also introduces a case study from the neurology field, which is used as a running example throughout the paper to concretely highlight the concepts presented in the paper. In Section IV , the overall architecture of a health management system is presented and its components, data sensing, acquisition and communication, aggregation, and pre-processing are further discussed in Section V and Section VI , respectively. Analytics and inference in healthcare applications are surveyed in Section VII , followed by a discussion of medical data visualization techniques in Section VIII . Section IX investigates security and privacy considerations of HIoT. Finally, Section X concludes the paper with our vision for the future.

II. TRENDS, APPLICATION DEMANDS, AND CHALLENGES

The majority of prior surveys on smart healthcare limit their discussion to specific aspects of the field such as sensing [ 21 ], [ 22 ], communication [ 23 ]–[ 25 ], data processing [ 26 ], and security [ 27 ]. Taking full advantage of the smart healthcare concept is contingent upon understanding the synergy of multiple mega-trends happening concurrently in the smart healthcare ecosystem [ 28 ] and how these trends affect the clinical practice of medicine and healthcare. In this section, we first highlight some of the major technological and societal trends that are driving HIoT adoption and then summarize the demands and challenges of HIoT applications.

A. Technological and Societal Trends

Data acquisition.

Unobtrusive, inexpensive, and accurate sensors are the work-horses for smart healthcare systems. Such sensors can replace the current practice of in-clinic sporadic sensing with continuous monitoring. Although the problem of building ideal smart healthcare sensors is far from being solved, several recent advances in sensing technologies have alleviated many of the existing challenges. Continuously diminishing feature sizes, integrated circuits have reduced the physical dimensions and power consumption of on-chip sensing devices while offering impressive computational capability.

Ambient energy harvesting proposals enable practical in-vivo sensors that no longer require a battery [ 29 ], [ 30 ]. Radio Frequency-based (RF-based) ambient sensors are working toward the measurement of multiple biomarkers such as respiration [ 31 ], [ 32 ], heartbeat [ 33 ], and motion detection [ 34 ], [ 35 ], although practical designs are still an active research area.

The first generation of personal health monitoring devices, such as smartwatches [ 2 ], has addressed several system integration issues and a software-user ecosystem has emerged that can be re-purposed to effectively integrate HIoT into clinical healthcare to enable better-informed decisions and care. In an important development, illustrated in Fig. 1 , such data is also being complemented by data collected from alternative sensors —that are not necessarily designed for healthcare applications, e.g., ambient sensors— can be fused with information from personal health monitoring devices to provide context awareness .

Modern smart healthcare applications are intricate multidimensional systems that not only focus on the personalized acquisition of physiological data but also incorporate information from various external sources such as past records of patients from their hospitals, research and educational resources, and even environmental information from smart city applications.

Data Communication

Low-delay, high-throughput, and low-power communication is an integral requirement of many smart healthcare applications. Two factors are crucial to the evolution of HIoT: (i) hierarchical network structuring (e.g., using cloudlets [ 36 ]) (ii) and the maturity of Wireless Body Area Networks (WBANs) [ 37 ]. A complete review of healthcare communication technologies can be found in [ 15 ].

Data Processing

By generating an unprecedented volume of information, burgeoning IoT services have significantly contributed to the big data phenomenon. Advances in parallel computation architectures, such as Graphics Processing Units (GPUs), promise to substantially reduce the amount of time necessary to perform sophisticated computations on acquired data. Furthermore, advances in data analytics and inference promise to extract information that can open the door to new cures for diseases and drastically improve diagnostic quality by providing superior decision support to healthcare professionals. ML algorithms have specifically made it feasible to predict the onset of fatal incidents such as seizures and heart attacks [ 38 ]. The impact of these advances has reached far beyond data processing; for example, signal processing and ML algorithms can now be used as an effective defense against noise [ 39 ], [ 40 ], offsetting the imperfections in data acquisition and communication.

Security and Privacy

Security and privacy considerations in smart healthcare systems have long been overshadowed by the design objectives of other system components; application-oriented services often trade off security and privacy considerations for shortened design time. However, the focus is returning to security and privacy in the wake of a surge in large scale cyberattacks targeting a vast range of IoT services. It is now evident that the lack of cybersecurity in different components of smart healthcare (and other IoT applications) is manifested as multi-faceted security flaws with ramifications ranging from privacy violations to endangering patients’ health. Consistent implementation of security and privacy preservation measures is now the primary trend in the field, as it is now known that one-dimensional communication security protocols do not provide immunity against complicated attacks. Researchers are constantly working to enhance the security of data acquisition (in areas such as countermeasures against physical attacks, side channel attacks, and malicious firmware overrides [ 41 ]) and data processing (in areas such as cloud-oriented data privacy and security).

Societal Trends

In parallel to the aforementioned technological advances, emerging societal trends are also driving HIoT adoption. These technological and societal developments are indeed the systole and diastole of modern healthcare infrastructure; the former assures feasibility and practicality, while the latter promotes HIoT from a mere nicety to an absolute necessity. At a societal level, the requirement for personalized and continuous healthcare is chiefly fueled by worldwide population aging [ 42 ]–[ 46 ], which is expected to further increase ever-soaring healthcare expenses. While the impact of aging populations is being felt in most societies, developed countries are facing this most urgently.

In parallel to these demographic changes, two additional societal trends further drive the demand for HIoT. The first of these is the expansion of middle-class families worldwide [ 47 ], particularly in countries such as China, India, and Brazil. With education and continuous access to the information resources of the Internet, this technologically savvy class is becoming more and more cognizant about personal healthcare. This directly translates to a growing market for a variety of devices spanning from smartphone-based services to smart homes, smart wearables, Air Quality (AQ) monitoring, etc. A second trend involves the increasing concentration of physicians and medical care facilities in large urban areas leaving sparse coverage in large geographically spread rural areas. In these latter settings, HIoT technology can effectively expand the geographic footprint of clinics and provide effective remote health management solutions.

B. Application Demands and Challenges

While HIoT systems should be specifically designed to fulfill the requirements of their target application, system designers should be aware of the limitations that will act as a barrier to proper functionality. We now highlight some of the most common demands and restrictions of HIoT devices, which may or may not apply to every possible application.

Physiological and Environmental Signals

The first demand of any HIoT system is determining the type(s) of physiological/environmental signals required for its intended application(s). A healthcare management system inherently requires a certain level of accuracy for acquired signals, and clear boundaries for the noise imposed on those signals [ 48 ]. Some of the major physiological attributes used in more common applications are shown in Fig. 1 , though it should be noted that different applications generally require various types of signals and data.

Decision Support

Decision support plays a crucial role in an HIoT management system. The data collected from various sources should be analyzed by the machine and presented to healthcare professionals in a comprehensive format, making the machine a support tool for the professionals [ 49 ]. The type(s) of decision support provided by an application can vary based on its purpose. For example, an application may require an automated warning system that issues an alert on critical conditions. This alert may be issued to the healthcare organization (HCO), to the doctor, to the patient’s caregiver, or directly to the patient.

Latency Tolerance

Latency tolerance can also affect the design of a system. Applications that deal with patients in critical condition, who need constant real-time monitoring, must be able to issue alerts with minimal delay [ 50 ]. Other applications targeted for less critical conditions, however, may be able to tolerate higher delay.

Computational Intensity

The volume of data gathered by the system requires a proportional amount of computation power to analyze it. Factors such as the number of sensors, signal sampling frequency, sample accuracy, and overhead imposed by encryption schemes all affect the computational power necessary for the system [ 51 ]. In addition, applications requiring lower latency need higher computational capabilities. Machine learning algorithms used in a system may also impose additional computational intensity, independent from the absolute volume of data; for example, two algorithms working on the same input data may require different computational intensity based on their nature.

Power Consumption

The sensors used to gather physiological signals are generally wearable, meaning they will be powered by batteries. Optimizing power consumption to extend battery life (using both hardware configurations and energy-aware algorithms [ 52 ], [ 53 ]) is a necessary step toward ensuring continuous signal acquisition and monitoring.

Data Communication Rate

In most HIoT systems, physiological signals are transmitted to a local concentrator through a WBAN [ 54 ]–[ 57 ]. The communication link between the WBAN and the concentrator has a bandwidth restriction, imposing a limit on the amount of data acquired per unit time. Some pre-processing may need to be performed on the acquired data before transmission to a local concentrator. and, in some cases, the size of the data after pre-processing can be greater than when it was transmitted from the WBAN (e.g., some encryption schemes impose a significant overhead).

III. HIOT EXAMPLE APPLICATIONS

We now highlight some common HIoT management applications and introduce a case-study that we will use for highlighting the relevance of HIoT in the clinical healthcare setting.

Activity Recognition

Activity recognition is prevalent in various areas of the healthcare domain, where multiple techniques are used for this purpose such as computer vision [ 58 ], active sensor beacons, passive radio-frequency identification (RFID) [ 59 ]–[ 62 ], WiFi [ 63 ], radar [ 64 ], etc. Most traditional activity recognition platforms, however, suffer from a high rate of false-positives when detecting abnormal activity. Applying learning techniques, such as Support Vector Machines (SVMs) [ 65 ] or dictionaries [ 66 ], can help mitigate this problem. Some examples of activity detection applications include: fall detection [ 34 ], [ 67 ], [ 68 ], fitness tracking [ 69 ], [ 70 ], human localization and tracking [ 71 ], [ 72 ], posture recognition [ 73 ], and gait abnormality detection [ 74 ], and multiuser activity recognition [ 75 ].

Stroke Rehabilitation

Rehabilitation among stroke patients is an important task that has been studied in many aspects. Recent trends have increasingly turned to self-managed rehabilitation [ 76 ]. Providing a virtual environment for patient rehabilitation [ 77 ], [ 78 ], predicting the strength of muscles based on kinematics [ 79 ], and monitoring a patient’s activities to provide feedback and assess the patient’s recovery process [ 80 ] are all examples of how HIoT can help with stroke rehabilitation.

Blood Glucose Monitoring

According to the Centers for Disease Control and Prevention (CDC), approximately 30.3 million people (or 9.4% of the US population) across all ages are currently living with diabetes [ 81 ]. Blood glucose monitoring can be especially useful for diabetic patients, as it can provide important information related to managing the disease [ 82 ]. In addition to regular monitoring, IoT devices can issue warnings in extreme/dangerous cases [ 83 ], provide suggestions on adherence to treatment regimens [ 84 ], and even help track patients’ meals or make healthy eating suggestions [ 85 ].

Cardiac Monitoring

One out of three deaths in the US are associated with cardiovascular disease, making it the leading cause of death in the nation [ 86 ]. Personalized and continuous cardiac monitoring (as opposed to conventional in-hospital monitoring) plays an integral role in lowering the fatality rate and associated expenses of cardiovascular diseases [ 87 ]. Existing studies investigate the efficacy of in-vivo and exvivo sensors in prediction and detection of various cardiac hazards such as arrhythmia [ 88 ], [ 89 ], long QT syndrome [ 90 ], and sudden cardiac arrest (SCA) [ 91 ]. These efforts have resulted in the recent emergence of noninvasive Food and Drug Administration (FDA)-cleared cardiac monitoring systems that can help diagnose a variety of heart rate variations (HRV)-related syndromes [ 92 ]. Regardless of their increasing commercial popularity, cardiac monitoring devices have recently been subject to scrutiny due to their potential security and privacy flaws. New regulations are expected to emerge to enforce more strict requirements on these systems [ 93 ].

Respiration Monitoring

Patient respiratory activity is a clear indicator of their overall health. Respiration monitoring devices are categorized as either contact (most common [ 94 ]) or non-contact [ 95 ]. Monitoring newborns for sudden infant death syndrome [ 96 ], monitoring the effects of medication(s) on respiration [ 97 ], and asthma patient monitoring [ 98 ] are all examples of IoT respiration monitoring devices.

Sleep Monitoring

Monitoring patients during sleep is another useful implementation of IoT devices. Some applications include monitoring and classifying sleep stages [ 99 ], monitoring vital signs during sleep [ 100 ], and detecting sleep disorders such as obstructive sleep apnea [ 101 ], [ 102 ].

Blood Pressure Monitoring

High blood pressure currently impacts approximately 45.6% of US adults [ 103 ] and, as a result, is a crucially-important health concern to address. IoT-based general hypertension monitoring systems are already in use [ 104 ], while some applications can even take control of hypertension decision-making processes [ 105 ]. Other blood pressure related issues, such as hypotension (low blood pressure), can also be monitored by IoT devices [ 106 ].

Stress Monitoring

Many systems have been developed to monitor various types of stress, and even intervene if necessary. While low-stress levels are normal, high-stress levels can lead to serious health issues [ 107 ]. Some applications attempt to reduce stress by offering suggestions on a mobile device [ 107 ], [ 108 ], monitoring post-traumatic stress disorder patients [ 109 ], and helping people on the autism spectrum manage their emotions [ 110 ].

Medical Adherence

Ensuring that patients adhere to their healthcare/medicinal regimen is a grand challenge in healthcare [ 111 ], [ 112 ]. Monitoring adherence for elderly patients [ 113 ], people with dementia [ 114 ], or general medical adherence monitoring [ 115 ]show how IoT devices can help ensure that regimens are followed properly.

Alzheimer’s Disease (AD) Monitoring

AD affects 5.3 million people in the US and incurs an estimated annual cost of $200 billion [ 116 ]. AD patients generally require constant, round-the-clock care. IoT-based devices can provide considerable assistance to caregivers in many areas, such as early detection of dementia [ 117 ], reporting anomalous activities [ 118 ], monitoring patient location, and providing task reminders [ 119 ].

Parkinson’s and Huntington’s Disease (PD/HD) Monitoring (Case Study)

Parkinson’s and Huntington’s are neurological diseases characterized by movement disorders. It is estimated that more than 900,000 individuals in North America will suffer from PD by 2020 [ 120 ] while HD currently affects more than 20,000 individuals in the US [ 121 ]. Body worn HIoT sensors provide an effective mechanism for monitoring the movement symptoms associated with PD/HD and are an promising option for assessing disease status, progression, and medication efficacy.

To provide concrete examples of the ideas discussed throughout the paper, we use a PD/HD case study from our recent and ongoing research [ 124 ], [ 125 ]. Motivating background information on these disease conditions and the related demands and challenges are summarized here. Subsequent sections in the paper highlight examples of relevant components from the case study through brief remarks.

Typical symptoms of PD/HD are depicted in Fig. 2 . Parkinson’s is characterized by rest tremors, slowness in movement, rigidity, and postural instability. Huntington’s is a genetic disease marked by jerky movements in the body (referred to as chorea), unsteady gait, and cognitive impairments [ 126 ]. Both diseases are progressive; after onset, patients encounter increasingly severe symptoms as time advances.

Graphics showing typical symptoms of Parkinson’s (left) and Huntington’s (right) disease that are the focus of the multisensor case study that we will use to illustrate the ideas discussed in this paper. Based on [ 122 ], [ 123 ].

PD and HD are currently considered incurable. Although medications can be used to manage symptoms, their effect is neither universal nor complete. The overall quality of life is often severely degraded for PD/HD subjects. Disease progression and efficacy of medications are currently assessed subjectively by physicians via in-clinic-tests that assign patients a score on the Unified Parkinson’s Disease Rating Scale (UPDRS) [ 127 ] or the Unified Huntington’s Disease Rating Scale (UHDRS) [ 128 ]. These ratings are subject to variability because they are based on sporadic observations that sample relatively short durations of time and because of inherent variability in human assessments. There is, therefore, a strong desire to develop sensor-based quantitative measures and scales that can be used to objectively assess disease progression and efficacy of treatments. This clinical application is ideally suited for HIoT because miniaturized unobtrusive sensors for movement have become ubiquitous and cheap due to the smartphone revolution, as have circuits for low power wireless communication and Internet connectivity infrastructure.

IV. SYSTEM ARCHITECTURE

The realization of inexpensive, unobtrusive, and reliable systems that can meet the requirements discussed in Section II – B necessitates a robust and inclusive design framework. To this end, various HIoT system architectures have been discussed in the literature [ 129 ]–[ 133 ]. In this section, we investigate the general architecture of typical HIoT systems [ 10 ] (as seen in Fig. 3 ) and outline each of the layers. Each layer is then discussed in more detail in subsequent sections.

High-level system architecture illustrating HIoT integration into clinical healthcare. IoT sensors record measurements for a range of physiological and health-related physical attributes. The data is communicated over the network and aggregated in the cloud by IoT concentrators. Cloud-based analytics and inference algorithms operating on the data provide decision support to physicians via visualization interfaces, dashboards, and real-time alerts to individual users. Security and privacy solutions must be implemented to include other components, ensuring data protection from acquisition to storage.

Data Acquisition, Sensing, and Transmission

The first HIoT component is data acquisition, where IoT devices and sensors measure physiological and environmental signals. These devices are connected to a WBAN, generally through an intermediate data aggregator such as a smartphone [ 36 ]. The primary function of the sensors is to sense and gather data, but many are also now able to preprocess data before transmission. Data acquisition is further detailed in Section V .

Data Aggregation/Preprocessing Cloudlet

The data gathered in the acquisition and sensing layer are transmitted to an IoT concentrator through a WBAN (using mediums such as WiFi, Bluetooth, or ZigBee Standard). These concentrators are responsible for gathering all the sensed data and transmitting it to the HCO data center within the delay tolerance requirements of the application. To fulfill this task, concentrators need to be more computationally capable (compared to devices in the previous layer) and must have a certain amount of local storage for data. We detail data aggregation and preprocessing in Section VI .

Cloud Processing and Storage

Once the data arrives at the HCO data center, it can be processed using advanced algorithms to provide decision support including data summarization and creation of visual representations. Since the cloud processing and storage layer is responsible for various tasks, we discuss it in multiple sections. Analysis of data using ML algorithms to extract useful insights is studied in Section VII , and data visualization is explored in Section VIII .

Privacy, Security, and Quality of Service (QoS) Management

As highlighted in Fig. 3 , the entire system must provide robust security and privacy guarantees while satisfying the QoS requirements of applications. Overemphasizing security within a single component (typically communication) does not translate to improved overall security because adversaries can exploit any susceptibility to infiltrate not only the HIoT system but also other connected services. The implementation of such an inclusive security structure, however, is challenging. Aside from the common limitations of IoT applications (such as meager energy budget, on-site deployment, and large-scale), HIoT services are subject to domain-specific complications, such as, the inherent sensitivity of health data, highly dynamic roles of stakeholders, heterogeneity in electronic health records (EHRs), and potentially grave consequences from failures. Section IX further lays out the details of HIoT security.

V. DATA ACQUISITION AND SENSING

In this section, we review some of the most commonly used sensors and investigate their invasiveness, cost, and accuracy.

A. Activity Detection Sensors

Activity detection is typically conducted through Inertial Measurement Units (IMUs), which are composed of multiaxis accelerometers, gyroscopes, magnetometers, and force sensors. IMUs can be implemented as wearable sensors [ 134 ] or sensors integrated into the environment [ 135 ]. Wearable sensors are typically inexpensive and can cater to a broad range of applications; multiple wearable sensors can easily be deployed to determine the relative position of body parts [ 134 ]. In some cases, wearable sensors are inconvenient for long-term use as their bulk and weight, coupled with their requirement to be in close proximity to the patients, impede the acquisition of continuous and real-time data. In these situations, ambient sensing techniques can be employed (e.g., cameras and RF sensing). Cameras are relatively easy to set-up and employ, as they do not require extensive changes to the environment. They, however, suffer from limited line-of-sight and pose privacy concerns.

B. Respiration Sensors

Respiratory motion can be captured using accelerometers or piezoelectric materials. The solid-state nature of piezoelectric sensors enables them to be reliable, small, low-power, and non-invasive. Authors in [ 136 ] demonstrate a complementary metal-oxide-semiconductor (CMOS) sensor for respiration monitoring that is implemented using a polyvinylidene-fluoride (PVDF) material to ensure bio-compatibility. The sensor consumes up to 800 μ W and, since direct contact with the skin is not required, can be embedded inside a jacket or worn around the chest. Alternatively, camera-based sensing solutions apply image processing algorithms to videos, capturing patients’ respiratory motions. Instead of tracking the slight motions of the torso (which has been demonstrated to be a demanding task), the majority of the proposed works rely on analyzing variations in the ambient light caused by body movement [ 137 ], [ 138 ].

C. Heart Beat Monitoring Sensors

Generally, heartbeat monitoring techniques are classified into four groups: (i) electrocardiography (ECG) (ii) ballistocardiography (BCG) (iii) phonocardiography (PCG), and (iv) photoplethysmography (PPG).

Holter devices [ 139 ] are common tools that capture ECG data through electrodes over long periods of time with various sampling frequencies. Non-contact, capacitive-coupled electrodes are also commonly used for ECG signal acquisition as they do not require direct contact with the skin, enabling their use as wearable sensors embedded in clothing [ 140 ]. These less invasive methods mean trading accuracy for comfort/wearability. While BCG signals can be captured in numerous ways, accelerometers are typically the primary choice [ 141 ]. Accelerometers can be used as wearable sensors if worn in proximity to the heart. While PCG signals are typically captured by microphones, piezoelectric sensors installed on the throat [ 142 ] and fiber optic sensors [ 143 ] can also be used. Finally, due to low-cost, non-invasiveness, and high reliability, pulse oximeters are generally used to capture PPG signals. Various physiological parameters, including Heart Rate Variability and even ECG signals, can be extracted from PPG signals [ 144 ]. Pulse oximeters, however, are not suitable for long-term continuous monitoring as they are typically worn on a fingertip.

D. Blood Pressure Sensors

Authors in [ 145 ] propose a wireless and battery-less sensor to be surgically implanted in the femoral artery to monitor hypertension patients. A second, bulkier device needs to be worn by the patient to power and interrogate this in-vivo sensor. Although invasive, the proposed solution is comfortable and suitable for long-term monitoring. Less intrusive techniques, which operate based on fundamentals of wave propagation dynamics in fluids, are also proposed in the literature. These techniques typically measure Pulse Wave Velocity (PWV) or Pulse Transit Time (PTT) to calculate blood pressure (BP). Data samples are collected using cuffless PPG, ECG, and Impedance Cardiography (ICG) sensors, which can be used as wrist bands and/or be worn around the chest [ 146 ], [ 147 ]. Relying on PTT poses additional challenges to BP monitoring; one challenge arises from the dependence of PTT on heart rate, age, gender, and body shape of the patients. These obstacles, however, can be partially addressed by employing suitable signal processing solutions [ 147 ], [ 148 ].

E. Blood Glucose Monitoring Sensors

Blood Glucose Monitoring (BGM) can be categorized into two different classes: (i) electrochemical-based and (ii) optical-based. The former analyzes the chemical content of interstitial fluids, while the latter involves spectroscopy techniques. Electrochemical sensors are typically more accurate but are more invasive. Common models of electrochemical sensors require blood samples, typically obtained from the fingertips of patients. Electrochemical-based sensors can also be implanted underneath the skin [ 149 ]. Because of the correlation between glucose level in sweat and blood, less invasive monitoring can be implemented by deploying flexible, small, and stretchable sensing patches that analyze sweat samples [ 150 ].

Spectroscopy is widely used as an alternative technique to provide a less invasive but less accurate BGM solution. Chemical composition of the blood (including glucose level) changes its ability to absorb, reflect, and scatter light beams. Spectroscopy uses this to estimate the blood glucose level. Therefore, optical-based spectroscopy sensors typically encompass a Near Infra Red (NIR) light source and a photon counter, which are installed on opposite sides of the tissue [ 151 ]. Table I provides a summary of aforementioned sensing technologies along with their primary strengths and shortcomings.

A list of commonly used sensors in various clinical HIoT applications. The qualities listed under ‘characteristics’ are relative. For example, for activity detection applications, RF-based sensing typically yields lower accuracy than wearables. Hence, it includes the ‘low accuracy’ attribute. This should not imply that RF accuracy is not practical.

F. Wearable Multisensors in the PD/HD Clinical Study

The PD/HD case study, outlined in Section III provides an excellent example of how multiple sensing modalities can be combined in one sensor. In this study, BioStampRC sensors from MC10 Inc. are used, which are lightweight (≈ 7 grams) and unobtrusive devices capable of operating in multiple modalities. The sensor (specifications shown in Table II ) operates with various sampling rates and dynamic ranges with high-power and long-duration recording capabilities. The sensors are applied to subjects’ body at five different locations as depicted in Fig. 4 . We primarily utilize the sensors’ accelerometer to obtain data, sampled at 31.25Hz, over the duration of 46 hours.

A graphic showing five different locations on the body for applying BioStampRC sensors in our PD/HD case study [ 152 ] (left) and a participant wearing sensors at these locations for in-clinic assessment [ 125 ] (right).

C apabilities of the B io S tamp RC sensor from MC10, I nc. used in our case study . T he sensor operates in 6 modes . g indicates the acceleration due to gravity (9.81 m /s 2 ).

VI. DATA COMMUNICATION, AGGREGATION, AND PRE-PROCESSING

In this section, we investigate two main aspects of HIoT communication: (i) connectivity, and (ii) data aggregation.

A. Data Communication

A WBAN consists of multiple low-power, resource-constrained devices that are connected to a more computationally capable device, such as an Access Point (AP), through a low-range and low-rate wireless link. The AP performs multiple pre-processing , data aggregation , and data-fusion operations on the collected data. Most importantly, it provides Internet connectivity. Broad trends in communications for HIoT have been surveyed in [ 15 ]. The majority of implementations in healthcare applications rely on either BLE [ 153 ]–[ 155 ] or ZigBee [ 156 ], [ 157 ].

BLE specifically targets low-rate, low-power, and low-range IoT communications [ 158 ]. It operates in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band, and can provide up to 2 Mbps bandwidth. To suppress the adverse effects of interference and fading, the protocol leverages adaptive frequency hopping techniques. Many existing portable devices such as smartphones and laptops are shipped with embedded BLE modules [ 159 ]. BLE, however, does not provide multicast communication, which can be crucial to many applications. Furthermore, as it only supports single-hop star topology, it cannot be adopted by multi-level hierarchal architectures. This limits its scalability and raises security concerns [ 158 ].

ZigBee is developed atop the IEEE 802.15.4 standard. It is designed for low-power, short-range, and low-rate data connectivity. Unlike BLE, ZigBee supports mesh architecture, which results in more robust implementations. Furthermore, it supports multicast [ 158 ]. Table III provides a comparison between ZigBee and WiFi in the context of smart healthcare applications.

A comparison of two commonly used communication protocols for WBAN implementation, along with with their advantages and disadvantages within the context of smart healthcare.

Note that the composition of this network should be defined prior to the application and verified after the setup. In our PD/HD case study, the number of sensors, their locations on the body, recording modes, and sampling rates are determined through the Investigator application before the network setup and their functionality is verified after the sensors are attached.

B. Data Aggregation and Pre-processing: Front-end

For data aggregation and pre-processing, an aggregator (typically added as a functionality of the AP) is used to collect and combine all sensed data before transmission. This step also includes performing preliminary computations on the data. This concept has seen more interest recently, especially with the introduction of fog computing [ 165 ]. Fog computing provides multiple benefits including low latency in some applications (more critical in emergency medical situations) [ 166 ], mobility support and location awareness [ 167 ], and reduced power consumption by replacing cloud communication with local computation (as communication consumes orders-of-magnitude more power than computation) [ 168 ].

Other roles and benefits of this stage include condensing data from multiple sensors into single packets to reduce communication overhead, removing redundant data that do not provide useful information for the system, and representing data from multiple sources using a single value (such as their arithmetic mean or median). For example, in a 12-lead ECG data acquisition system, it is shown that the same intervals from different leads can be median filtered to provide a final, single value, thereby reducing data volume by a factor of 12 [ 90 ]. Additionally, in many applications, data from many different sources complement each other to provide a bigger and better picture of the situation. The aggregation process keeps data from all sources in sync with each other so that the concurrency of events recorded by different sensors are maintained. The aggregation and pre-processing stage tends to lose information.

C. Data Aggregation and Pre-processing: Back-end

Many HIoT applications resort to cloud-based solutions not only to circumvent the challenges of processing large data volumes but also to take full advantage of cloud’s compatibility with off-the-shelf data analytics, always-on property, scalability, and affordability [ 169 ]. Particularly, the cloud can provide permanent data storage services (ultimate data aggregation), which are the basis of data analytics and inference. Long-term data storage is also a prerequisite for history-based verification mechanisms that can evaluate the veracity of data by comparing them to the expected values. Long-term data are also valuable assets to HCOs and other entities. For example, health insurance agencies use such data to evaluate the overall health of applicants and charge them accordingly [ 170 ]. Cloud storage also provides a point of access to retrieve information.In our PD/HD case study, all of the data recorded by the sensors are eventually transferred to and stored in the cloud. After the data is uploaded to the cloud, researchers can access, preview, or download it through the Investigator portal on demand, as seen in Fig. 5 . Each subject’s information can easily be downloaded without any loss of information.

Screen shot of MC10 web portal used in our PD/HD case study (named Sensor-MD Condensed 2 ). We can identify six subjects with unique subject ID (005), sex (M/F), and age (years) indicated on the right side. Total duration of recorded data is indicated on the right of an icon with a green tick mark. Clicking on the green tick marked icon downloads the data which can be utilized for analysis. On the far right, we can observe the statistics of the study showing the total number of subjects in the study followed by graphs showing number of male and female participants and age distribution.

HCOs can opt for public , private , or hybrid cloud services. Public servers can simultaneously host multiple applications from different entities. This reduces operational expenses but increases the risks of privacy leakage. Alternatively, HCOs can set up and maintain their private servers trading off additional expenses for improved security. As a compromise between the two implementations, it is also feasible to outsource demanding computations to public servers while processing sensitive data locally in a private setup. From another perspective, cloud-based servers can be centralized or distributed. The latter can reduce maintenance costs by taking advantage of geo-diversity. However, distributed servers are known to be more difficult to manage and require more complicated task distribution and resource allocation [ 171 ].

VII. ANALYTICS & INFERENCE

The volume of healthcare data generated in recent years from bio-sensors, EHRs, computerized physician order entry, social media, and administrative entries, was estimated to be 153 Exabytes in 2013 and is expected to reach 2000 Exabytes in 2020 [ 172 ]. This impressive accumulation of data in HIoT has created a conducive environment for data analytics and inference algorithms, which are now used in a variety of healthcare applications for anomaly detection, prediction of future health events, early detection of diseases, cost reduction, improved accuracy in clinical diagnosis, and clinical decision support [ 173 ], [ 174 ].

Despite its potential to revolutionize clinical HIoT, integration of data analytics and statistical inference techniques in clinical practice has been slow. The few documented successful examples are localized within HCOs with rich datasets. We dedicate the remainder of this section to providing an analysis of the two key drivers, which can enable a widespread adoption for clinical HIoT. In Section VII – A , we provide a brief overview of the existing and emerging algorithms that can form the backbone of clinical HIoT, followed by Section VII – B , where we study the issues arising due to the availability of the data that can be used in these algorithms.

A. Algorithms

Data analytics and inference algorithms can make HIoT an indispensable decision support tool for healthcare professionals. Although a good portion of the algorithms used today has existed for decades (e.g., Support Vector Machines and decision trees), they have not seen widespread use until the recent explosive growth in the computational capabilities of computing hardware [ 175 ]. This growth made extremely computationally-intensive algorithms, which were previously considered unusable, practical in cloud computing platforms with vast resources (e.g., Amazon EC2). Application of these algorithms has also been accelerated due to the wide availability of open-source software toolboxes that provide rapid development environments. Most of these algorithms are not only good at discovering explicit relations among data but also the latent and hidden features that are very difficult to detect by human specialists [ 176 ].

Most of the algorithms that existed in the previous decade required intricate knowledge of the features that were associated with a health condition; for example, the study in [ 90 ] attempts to use inference algorithms to determine the existence of known cardiac conditions in patients. They start their application by extracting features from ECG signals. This implies that the success of the inference depends strongly on the understanding of the features. The emerging deep learning (DL) network-based inference techniques largely eliminate this feature extraction step by utilizing a network that not only provides inference, but also extracts the features. DL-based inference applications have seen significant recent success in clinical practice [ 175 ], [ 177 ], however, their success has been restricted to applications that can provide the vast amount of data that deep networks demand to achieve acceptable accuracy.

Algorithmic nuances of data analytics and inference techniques pose additional obstacles against their commercialized use in clinical HIoT. A major challenge concerns the interpretability of an algorithm’s decisions. In fact, physicians are often more interested in the algorithm’s thought process as opposed to its final decision as this enables them to better assess the reliability of the inference [ 178 ]. Additionally, the reliance of analytics and inference techniques on statistical analysis sometimes limits their ability to model outliers, which are often of interest in the medical diagnosis of rare conditions. The evolving nature of progressive learning algorithms also complicates approval processes for clinical deployment; although the FDA has recently passed new regulations, where algorithms are cleared based on their developing teams [ 179 ].

Data analytics for our PD/HD case study involved both classification and regression [ 125 ]. The former involves categorizing patient activity into one of four classes: {Lying down, Sitting, Standing, Walking}. In our study, activities were classified every five seconds and the activity states for each interval was identified by determining the dominant acceleration axis (the axis with the largest mean acceleration). Based on the dominant orientations in chest and thigh sensors, activity states were categorized as lying down, sitting, and upright. A normalized auto-correlation-based analysis [ 124 ] was performed on the upright state intervals, which were further classified upright durations into standing and walking intervals. Figure 6 shows the proportion of time (over the full duration) subjects spent in different activity states.

Classification of the activity states in our PD/HD case study as percentage of time spent (total duration ≈ 46 hours) lying down, sitting, standing, and walking for Control, PD, HD, and prodromal Huntington’s disease (pHD) participants. “n” is the number of participants analyzed [ 125 ].

Our PD/HD case study uses regression to analyze (poor) inter-limb coordination in HD subjects. Sensor data from a 10-meter walk test was used. A normalized cross-correlation of recorded acceleration from the left and right leg sensors indicated coordination between the legs while walking. As shown in Fig. 7 , for the control participant, the strong peaks in the cross-correlations at the 1-step and 2-step durations are indicative of the rhythmic nature of normal walking, whereas the HD participant’s peaks are much smaller and poorly defined due to lack of coordination between the legs [ 124 ]. Apart from quantifying and visualizing leg coordination, this example specifically illustrates the benefit of using multiple body-worn sensors for the analysis.

Data analysis performed for our PD/HD case study to assess inter-leg coordination. The plot shows the normalized vector cross-correlation [ 124 ] of the recorded accelerations from left leg and right leg sensors as a function of time lags for a HD and a control participant. A 2-step cycle is annotated and highlights stronger peaks for the controls at the one and two step intervals compared with participants with HD.

B. Data Availability

Viii. visualization.

The primary objective of visualization is to present the results of data analytics and inference algorithms in the form of intuitive tables, charts, graphs, etc. to enable rapid and intuitive absorption and interpretation of the patient data by healthcare professionals. Compared to sporadic in-clinic measurements, HIoT datasets are large and visualization is an absolute necessity. Physicians seeing between 20–40 patients a day have no way to absorb and interpret the reams of numerical data that HIoT sensors can generate, effectively presenting the results via visualizations is also a challenging task [ 36 ]. Including too much detail can distract the attention of the user from critical information, while vital data can be omitted in aggressive summarization. Although visualization in clinical HIoT applications is primarily directed toward physicians, there is also a need to visually present some information to patients to facilitate understanding and improve engagement.

Visualization schemes can be static or interactive . Static visualization techniques range from lists/tables, plots/charts, graphs/trees, and pictograms to more complex formats showing spatial data, multidimensional data, or causal relationships [ 184 ]. Interactive methods are especially useful when visualizing information that simultaneously incorporates multidimensional temporal signals and generic static information.

A. Static Visualization

Systems such as hGraph [ 185 ] and its related programming libraries (such as the one introduced in [ 186 ]) provide visualizations by combining in-clinic, activity, sleep, blood pressure, and nutrition data. Other systems such as TimeLine [ 187 ] provide an EHR visualization, which shows all related static data (such as a list of medical problems, general information, and patient status) on a timeline. Another study targeting clinical information is presented in [ 188 ], where a real-time bedside graphic display is developed. The display includes personal information, lab results, vital signs for specific periods of time (e.g., over the past 24 hours), and Intensive Care Unit (ICU) information.

Choosing the proper visualization format ensures that medical professionals do not miss critical information. In our PD/HD case study, patient tremors over the period of one hour are plotted using a radial plot (as shown in Fig. 8 ). This allows easy comparison of tremors between healthy subjects and PD patients while identifying the difference in patient tremors when they are on vs off their medication. As more data becomes available, both for the individual, and across individuals, this figure can be further enhanced by adding in bounds for the “normal” range for tremors, or by customizing the presentation for each individual. Compared with the raw sensor data which for a single sensor is 8.2 MB over an hour and 196.1 MB over a 24 hour period (total across 5 sensors is approximately 1GB per day), the summarized presentation in the visualization is much easier for physicians to absorb and interpret.

Visualization example from our PD/HD case study. A clock based visualization shows the variation in at-rest tremor magnitude in individuals with PD and compares it against controls and between on and off medication states. The tremor magnitude is quantified as the fraction of power in the 4 to 6.5 Hz frequency band (radial axis) for the principal axis acceleration recorded from MC 10 sensors affixed to the forearm over an hour duration (angular axis): (a) PD vs control (left), and b) PD ON vs OFF medication (right).

Studies such as [ 189 ]–[ 191 ] focus on providing feedback on personal health status through visualization. This information may be presented in different forms, such as an abstract art display of physical activity [ 192 ], charts and graphs [ 190 ], or even through a physical form with data sculptures [ 193 ].

B. Interactive Visualization

Many applications focus on providing an interactive environment in their visualization scheme. For example, Care-Cruiser is an interactive system that visualizes the effects of applying patient treatment plans [ 194 ]. The system shows the treatment plan progress and depicts the patient’s condition at any given point during treatment. The system also shows hierarchical data (nesting of treatment plans and sub-plans), temporal data, and qualitative data, and provides a means to compare multiple patients. Another interactive system is Medical Information Visualization Assistant [ 195 ] that allows medical experts to obtain relevant information in a context-related environment. A more specific visualization called Interactive Parallel Bar Chart (IPBC) is proposed in [ 196 ], which depicts data gathered from multiple hemodialysis sessions in a 3D bar chart. Numerical data over time of each hemodialysis session is shown in one row and different sessions are bundled together to create the final plot.

IX. SECURITY AND PRIVACY CONSIDERATIONS

System security and data privacy are the highest priorities in a medical system and should be considered during every phase of system design. Additionally, designers must comply with the Health Insurance Portability and Accountability Act (HIPAA) [ 197 ], which mandates that all service providers ensure the privacy of their clients. In this section, we discuss security and privacy considerations for each layer of our proposed HIoT system.

Data Acquisition and Sensing

Strict energy-budget, on-field deployment, and limited computational resources leave data acquisition level vulnerable to a wide spectrum of cyber threats such as eavesdropping and man-in-the-middle attacks, making it an easy target for adversaries (particularly insiders [ 198 ]). Lightweight cryptography is the main countermeasure against attacks. It is very effective in protecting both the security and privacy of smart healthcare sensing; simple but effective algorithms such as Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) can be tailored for resource-constrained devices. These techniques, however, leave the system vulnerable to side channel and hardware attacks. Hence, in addition to cryptography-based solutions, HIoT devices must be equipped with platform integrity attestation mechanisms for protection against hardware/software tampering attacks [ 199 ].

Communications

Adversaries primarily target smart healthcare communication because (i) its properties are better-known (in comparison to other components), (ii) it can be addressed remotely, (iii) due to its interconnection with other networks (e.g., home network) it can provide a launchpad for targeting non-HIoT applications, and (iv) well-known attacks such as Denial-of-Service (DoS) can result in grave consequences.

Cryptography is the premise of many existing communication security measures. Particularly, AES and ECC are suitable for the resource-constrained nodes in smart healthcare systems [ 200 ]. Both BLE and ZigBee utilize AES in their link layers. The network layer can be effectively protected using IPv6 over low power wireless personal area networks (6LoWPAN), since it can incorporate the Internet protocol security (IPSec) protocol. IPSec improves communication security, regardless of the protective mechanisms implemented in the application layer [ 201 ]. Finally,to achieve end-to-end encryption, the application layer must use the constrained Application Protocol (CoAP), as it utilizes Datagram Transport Layer Security (DTLS) and IPSec. DTLS can be considered as the User Datagram Protocol (UDP)-compliant implementation of the transport layer security (TLS) protocol.

Cloud Storage and Processing

Modern authentication mechanisms aim to replace conventional password-based solutions, as passwords are susceptible to many attacks (e.g., brute force and dictionary attack) and can become an inconvenience (especially, in HIoT, where a portion of users are elderly and disabled people). Two-factor authentication (TFA) is a common methodology today for healthcare systems to avoid a breach in case one key is compromised. TFA requires the user to enter a password and a secondary piece of information (e.g., a code obtained through a cell phone call or a dedicated device) for successful login. Due to the sensitivity of collected data and EHRs, HIoT implementations must ensure secure access to data. The conventional access control techniques, however, are proven inefficient in HIoT as it involves a large number of stakeholders with many dynamic roles. Attribute-based access control can circumvent many of these limitations but it fails to dynamically adjust privileges (say) in an emergency situation [ 202 ].

To protect data during processing [ 203 ], Advanced encryption schemes, such as Fully Homomorphic Encryption (FHE) have been proposed. While there is research showing the feasibility of such schemes, their computational intensity renders them impractical. Additionally, applying multi-layer isolation (Operating System (OS), Virtual Machine (VM), and hardware) can reduce the odds of data leakage in public servers both in the data processing and data storage layers [ 169 ]. Finally, it is equally important to stress the non-technical aspects of HIoT security. HCOs must form security teams to oversee the security of the system and provide timely responses to threats and attacks. Stakeholders must be trained, and HCOs must be ready to negotiate with attackers to protect the data [ 204 ].

In the PD/HD case study, participant privacy is carefully managed. Within the MC10 portal where the sensor data for the participants is stored and made available for analytics, participants are identified only by assigned ID numbers. The portal provides basic demographic information such as sex and age (see Fig. 5 ) but does not provide any other personally identifying information. EHRs with personally identifying information used in the clinic are maintained in a separate Red-Cap database [ 205 ] that is HIPPA compliant. This partitioning of data effectively ensures the availability of the sensor data for analytics without compromising the privacy of the participants. Of course, the partitioning of the data notwithstanding, the study is performed in compliance with the Institutional Review Board (IRB) requirements: informed consent is required from all participants and everyone accessing the data is required to undergo human subjects training certification.

X. CONCLUSION AND VISION FOR THE FUTURE

This paper reviewed the state-of-the-art in Healthcare IoT (HIoT) technologies, particularly focusing on clinical applications of HIoT. It presented HIoT through the lens of three of its primary components: (i) sensing and data acquisition, (ii) communication, and (iii) data analytics and inference. As the underlying IoT technologies in HIoT become more mature, each one of these three components will independently witness rapid progress within their own domain. Data acquisition and sensing technologies will benefit from future VLSI technologies that require lower battery power for their operation, while communication standards will continuously advance to provide higher communication throughput with decreasing power consumption demands from the sensing networks. Intelligent energy-aware operating systems will also be critical to harnessing the energy demand of end-devices [ 206 ]. Future cloud platforms will take advantage of the ever-increasing computing power of the CPU and GPUs to enable more sophisticated machine learning algorithms to be run in the cloud with higher accuracy [ 207 ], [ 208 ]. As a consequence of this dizzying pace of progress, we envision significant opportunities that will eventually make HIoT an indispensable part of clinical practice in the coming decade. However, we also expect several barriers to entry that can slow the pace of adoption. In this section, we first review the challenges and then discuss opportunities.

Challenges that may prevent rapid adoption of HIoT can be broadly categorized as legal, regulatory, and ethnographic [ 209 ]. From a legal point of view, we particularly expect legal accountability to pose a challenge. Suppose a highly-trusted machine learning-based decision support system were to fail, causing bodily harm or fatality among patients. Can the machine be held responsible? If so, specifically, does the responsibility lie with the programmer, adopting institution, or the business entity that sells the program [ 210 ]? Questions of this type that were previously irrelevant, must be addressed to eliminate uncertainties and allow organizations adopting HIoT to better understand their legal exposure and risks.

The second set of challenges we envision are in the regulatory domain. HIoT devices are likely to span a wide range of applications and sensing/actuation modalities that vary in invasiveness. Accordingly, there are likely to be different classes of devices with different regulatory requirements [ 211 ]. While a majority of the non-invasive sensors may be made available for purchase over-the-counter (for instance, for gathering data prior to a routine check-up with a physician), other devices that are more invasive —and/or have potentially adverse side effects— will likely be available only upon prescription and will likely be subjected to regulatory approval after clinical trials [ 212 ]. As such, approval and adoption rates, as well as pricing of devices, will exhibit significant heterogeneity and the synergistic benefit from multiple HIoT sensors working in unison may take longer than anticipated [ 177 ], [ 213 ].

Finally, ethnographic challenges involve the reluctance of the medical community to adopt HIoT, due to its perceived risks vs. marginal added-utility in day-to-day clinical practice [ 214 ]. We anticipate the adoption may be slow until some avant-garde healthcare organizations start gaining significant advantage due to HIoT adoption and build a history of operation without glitches. If early adopters are able to exhibit significant operating advantages quickly, there is also the possibility that HIoT penetration may accelerate quickly and virtually become the norm within a short period of time.

Despite the challenges, the HIoT also presents significant opportunities. One of the biggest opportunities is the potential for much higher diagnostic accuracy that can be achieved by using statistical inference and data analytics algorithms with the increasing availability of clinical data [ 215 ]. Currently, limited public datasets are available for training ML algorithms and HCOs rely on their individual databases [ 216 ], [ 217 ]. Large datasets that are required to train sophisticated algorithms (such as ones that use Deep Learning) are not freely available. Widespread adoption of HIoT in clinical settings and the aggregation of the resulting data, with appropriate anonymization, can create shared large-scale datasets from which all organizations can benefit and improve diagnostics and health management [ 218 ]. We envision that large-scale anonymous medical data sharing networks, enabled by HIoT, will vastly improve ML accuracy, much like data availability is currently revolutionizing computer and machine vision applications. Once established, the trend is likely to be self-reinforcing with continued acceleration in data accumulation driving improved inference and finer-grained/personalized analyses. Such a positive feedback cycle is also likely to speed up the mainstreaming of HIoT.

Another opportunity lies in the potential future use of new sensing modalities and actuators. For example, while today’s sensor technology requires a blood draw for accurate measurement of blood glucose levels, future sensor technology promises to perform the same measurements far less invasively, without a blood draw [ 219 ]. Availability of such conveniently-deployable technologies (e.g., non-invasive blood pressure, blood glucose, and oxygenation sensors) can drastically increase their use in clinical settings. Future actuator technologies can also improve the automation of routine medical tasks, such as drug delivery. Advances in Micro-Electro-Mechanical Systems (MEMS) technologies promise to create actuators that can deliver drugs (e.g., insulin) into the bloodstream directly, eliminating the need for the patient to manually do so [ 220 ]. This can result in substantial improvements in patient health, as the need for the patient to perform routine measurements, followed by drug intake, are eliminated and the drug dosage and administration regimen can be customized based on both the specific medication involved and the individual [ 221 ]. Benefits of MEMS-based technologies are even more pronounced for patients that cannot administer the drugs themselves due to motor deficiencies.

In summary, although the HIoT roadmap into the future is not free from bumps, we expect the pace to quicken once the ecosystem is in place and early adopters demonstrate the benefits it can provide.

ACKNOWLEDGMENT

The authors thank the anonymous reviewers for several constructive suggestions that have significantly improved the presentation in the paper. This work was supported in part by the U.S. National Science Foundation grants CNS-1239423 and CNS-1464273 and by the U.S. National Institute of Health grant NINDS-1P50NS108676-01.

Hadi Habibzadeh (S’17) received his B.S. in CE from Isfahan University of Technology in Iran in 2015 and his M.S. degree from University of Rochester, USA in 2016. He is currently pursuing his PhD degree in the ECE department at SUNY Albany, under the supervision of Dr. Tolga Soyata in the field of Cyber Physical systems and embedded systems with applications in Internet of Things and Smart Cities.

Karthik Dinesh received B.E in Electronics and Communication from National Institute of Engineering, Mysore, India in 2010 and M.Tech from Indian Institute of Technology, Kanpur, India in 2013. He is currently pursuing PhD in ECE department, University of Rochester under the supervision of Dr. Gaurav Sharma.

Omid Rajabi Shishvan (S’16) received his B.Sc. in EE from Sharif University of Technology in 2012 and his M.Sc. degree in ECE from University of Rochester in 2015. He is currently pursuing his PhD degree at the ECE department of SUNY Albany.

Andrew Boggio-Dandry (S’17) graduated summa cum laude from The University at Albany, State University of New York (SUNY Albany) with his B.S. in Computer Engineering in 2018. He is currently pursuing his PhD degree at the ECE department of SUNY Albany.

Gaurav Sharma (S’88-M’96-SM’00-F’13) is a professor at the University of Rochester in the Department of Electrical and Computer Engineering, in the Department of Computer Science, and in the Department of Biostatistics and Computational Biology. He received the BE degree in Electronics and Communication Engineering from Indian Institute of Technology Roorkee (formerly Univ. of Roorkee), India in 1990; the ME degree in Electrical Communication Engineering from the Indian Institute of Science, Bangalore, India in 1992; and the MS degree in Applied Mathematics and PhD degree in Electrical and Computer Engineering from North Carolina State University, Raleigh in 1995 and 1996, respectively. Dr. Sharma’s research interests include data analytics, cyberphysical systems, signal and image processing, computer vision, media security, and communications. He is a fellow of the IEEE, of SPIE, and of the Society of Imaging Science and Technology (IS&T) and a member of Sigma Xi. He is the Editor-in-Chief (EIC) for the IEEE Transactions on Image Processing and previously served as the EIC for the Journal of Electronic Imaging from 2011 through 2015. He is a 2020 Distinguished Lecturer for the IEEE Signal Processing Society.

Tolga Soyata (M’08 SM’16) received his B.S. degree in ECE from Istanbul Technical University in 1988, M.S. degree in ECE from Johns Hopkins University in 1992 and Ph.D. in ECE from University of Rochester in 2000. He is an Associate Professor in the ECE Department of SUNY Albany. His teaching interests include CMOS VLSI ASIC Design, FPGA-and GPU-based Parallel Computation. His research interests include Cyber Physical Systems and Digital Health. He is a senior member of both IEEE and ACM.

Contributor Information

Hadi Habibzadeh, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

Karthik Dinesh, Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627.

Omid Rajabi Shishvan, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

Andrew Boggio-Dandry, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

Gaurav Sharma, Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627.

Tolga Soyata, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

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Please note you do not have access to teaching notes, a case-study to examine doctors’ intentions to use iot healthcare devices in iraq during covid-19 pandemic.

International Journal of Pervasive Computing and Communications

ISSN : 1742-7371

Article publication date: 9 November 2020

Issue publication date: 25 November 2022

Several countries have been using internet of things (IoT) devices in the healthcare sector to combat COVID-19. Therefore, this study aims to examine the doctors’ intentions to use IoT healthcare devices in Iraq during the COVID-19 pandemic.

Design/methodology/approach

This study proposed a model based on the integration of the innovation diffusion theory (IDT). This included compatibility, trialability and image and a set of exogenous factors such as computer self-efficacy, privacy and cost into the technology acceptance model comprising perceived ease of use, perceived usefulness, attitude and behavioral intention to use.

The findings revealed that compatibility and image of the IDT factors, have a significant impact on the perceived ease of use, perceived usefulness and behavioral intention, but trialability has a significant impact on perceived ease of use, perceived usefulness and insignificant impact on behavioral intention. Additionally, external factors such as privacy and cost significantly impacted doctors’ behavioral intention to use. Moreover, doctors’ computer self-efficacy significantly influenced the perceived ease of use, perceived usefulness and behavioral intention to use. Furthermore, perceived ease of use has a significant impact on perceived usefulness and attitude, perceived usefulness has a significant impact on attitude, which, in turn, significantly impacting doctors' behavior toward an intention to use.

Research limitations/implications

The limitations of the present study are the retractions of the number of participants and the lack of qualitative methods.

Originality/value

The finding of this study could benefit researchers, doctors and policymakers in the adaption of IoT technologies in the health sectors, especially in developing counties.

• Healthcare devices
• Technology acceptance model
• Innovation diffusion theory

Alhasan, A. , Audah, L. , Ibrahim, I. , Al-Sharaa, A. , Al-Ogaili, A.S. and M. Mohammed, J. (2022), "A case-study to examine doctors’ intentions to use IoT healthcare devices in Iraq during COVID-19 pandemic", International Journal of Pervasive Computing and Communications , Vol. 18 No. 5, pp. 527-547. https://doi.org/10.1108/IJPCC-10-2020-0175

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7 Amazing Use Cases of IoT in Healthcare

Table of content:, introduction.

When we think of IoT in healthcare, we mainly tend to think of smart sensors and smart hospitals. But the solutions go beyond these. It is evident from the fact that spending on IoT solutions in healthcare will reach $1 trillion by 2025!

IoT promises to help healthcare organizations in providing personalized, accessible, and up-to-the-point healthcare services at a lower cost. From remote health monitoring to transmitting real-time alerts, there are several areas where healthcare IoT finds its use.

7 Exciting IoT Use Cases in Healthcare

Let’s look at some of them to get a better perspective:

Remote Patient Care

In many parts of the world, residents live miles away from the nearest hospital. As such, when there is an emergency, it takes time for them to reach the healthcare facilities. Similarly, for healthcare providers, it becomes difficult to visit patients with chronic conditions frequently. The issue with time-consuming commute can be solved with remote patient care powered by the IoT.

The connectivity can allow healthcare professionals to assist patients with prescriptions, medication, and also measure their biometrics using sensors and remote equipment. For instance, patients can connect any wearable or portable device to the cloud and update the data in real-time.

Some of the IoT devices can also facilitate face-to-face talk over the internet. This can provide healthcare professionals with the necessary information to prepare care plans while the patients are on their way to the hospital. Or even without them needing to visit the hospital in the first place! For chronic patients, this helps create a roster of the patients’ day-to-date health update.

The collected data can form charts and diagrams to be easily visualized by healthcare professionals.

Live video and audio streaming can be used to monitor patients’ present condition without the need for the commute.

Emergency Care

Emergency care outputs are based on the time, accuracy, and the availability of contextual information. Moreover, it also depends on the quality of the data received during the emergency call and the information collected while the patient is being transported for immediate care at the healthcare facilities. Also, the entire process of collecting, storing, processing, and retrieving the data during that time is laborious and time-consuming. IoT can help in collecting data accurately, which can be accessed by emergency care staff such as paramedics or staff in the ER for quick and better medical assistance. This data can be also be transmitted to ER staff in real-time while the patient is on her way to the hospital – allowing the hospitals to be better prepared for the care.

Tracking of Inventory, Staff, and Patients

Healthcare organizations are all about increasing the efficiencies of their workforce and reduce operational costs. This is true for both small and large institutions that include several staff members, patients, and inventory. Using IoT devices in the form of wireless ID cards, hospitals can manage admissions, increase the security, and measure the overall performance of the staff. BLE (Bluetooth Low Energy) beacons and RFID tags can be used to track the location of the inventory and also trace the staff members in case of any urgency.

Moreover, IoT and RTLS (Real-Time Location Systems) together can facilitate asset tracking. This is one of the most inexpensive ways to keep track of the equipment, drugs, and free resources, who can then spend more time on patient care.

Must Read: How IoT Is Transforming Healthcare

Augmenting Surgeries

When it comes to healthcare, IoT has penetrated operating rooms as well. Think of connected robotic devices, which are powered by Artificial Intelligence and are used to perform various surgeries. These operations are all about increased precision brought forth by robot-assisted surgeons. Moreover, connected devices and IoT applications can perfectly streamline the activities of the medical staff at both pre and post-operating stages. In both cases, IoT sensors can be used to collect, transmit data, and analyze it. This helps record the tiniest details and therefore, is useful in preventing surgical complications.

Virtual Monitoring of Critical Hardware

It is a given that all the modern healthcare facilities require state-of-the-art hardware and software to function. When these are not taken care of in the best possible manner, the hardware can pose various risks and threats. Think of power outages, system failures, or even cyber-attacks. Since no healthcare organization would want these mishaps to occur, they opt for the best IoT driven solutions. A case in point is that of e-Alert by Philips , which can virtually monitor critical medical hardware. If there is an anomaly in any equipment, the solution alerts the hospital staff, so that a failure can be avoided by preventive maintenance.

Pharmacy Management

The pharmacy business is worth millions of dollars and is quite complicated. Since there are several steps in transferring and managing the drugs from plant to storage facilities in a hospital, there are several preservation issues that may be associated with them.  IoT can help combine the best safety approaches and the latest technology to ensure faster drug delivery, safer operations, and better patient care.

For instance, take the example of smart fridges, which can be used to store vaccines and keep them from getting damaged during handling, storage or transfer.

IoT-enabled pharmacies can ensure greater efficiencies and effectiveness in operations, error-free medical dispensing, security, and overall enhanced patient satisfaction.

Must Read: Key Challenges with Enterprise IoT Applications

IoT devices, in the form of wearables, can let the care teams collect numerous data points about the patient’s sleep patterns, activity, heart rate, temperature,  and so on. These wearables can offer real-time information to caregivers and patients. Think of a situation in which a heart patient has an elevated heart-rate. The wearable will immediately transmit the signal to the nursing staff and allow them to provide immediate and timely assistance to the patient. This can also help in remote health monitoring of elderly patients who are outside the hospital premises but need constant monitoring.

IoT in healthcare has tremendous potential and can prove to be immensely beneficial for healthcare providers and patients. It is set to transform patient care and organizational efficiencies. Several hospitals around the world have already leveraged the power of IoT under their smart hospital initiatives. At Pratiti, we have helped several healthcare organizations with the design and development of their smart healthcare software development solutions.

Connect with us  to know how we can help you improve patient care, reduce complexity, improve efficiency, and empower decision-makers with actionable insights at the point of care.

Frequently Asked Questions

How is iot used in healthcare.

Remote monitoring in the healthcare industry is now possible thanks to IoT devices, which have the ability to keep patients safe and healthy while also enabling healthcare providers to provide improved treatment. As communication with doctors has gotten easier and more efficient, it has also boosted patient interaction and satisfaction. Furthermore, remote monitoring of a patient’s health helps to shorten hospital stays and avoid re-admissions. IoT in healthcare has a big impact on lowering healthcare expenses and improving treatment outcomes.

What are the Advantages of IoT in Healthcare?

The advantages of internet of things in healthcare applications lies in remote use cases. For example, in the event of a medical emergency, real-time remote monitoring via connected IoT devices and smart notifications can detect illnesses, treat diseases, and save lives.

Smart sensors monitor health status, lifestyle choices, and the environment to suggest preventative steps that will limit the occurrence of diseases and acute states.

Medical data accessibility allows patients to receive high-quality care while also assisting healthcare providers in making the best medical decisions.

What are the Challenges of IoT in Healthcare?

Although there are numerous advantages as seen in internet of things in healthcare examples, there are also challenges.

Healthcare providers are frequently tasked with ensuring the security of several sites as well as vast data repositories.

Moreover, moving an entire facility to a new system and practice takes time, and the initial investment and installation costs can be prohibitive, particularly for smaller healthcare facilities and rural clinics.

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Milind Barve

Milind has 25 years of experience in building Enterprise Software. He is an Alumni of Indian Institute of Technology, Mumbai. He is currently the co-founder & CTO of Pratiti Technologies Driving technology implementations in IoT, Digital enterprise and Healthcare domains. Instrumental in building Business applications and data platform for Industry 4.0 application stack. He also has experience in building IoT applications for renewable energy asset monitoring, discrete manufacturing, logistics Smart Home platform etc. He has a vast Exposure to building enterprise solutions for large industrial customers, Enterprise Integrations & Data exchanges. He also holds expertise in designing and Manufacturing Engineering solutions.

Stefan Jockush

Stefan is Services Innovation Leader for Philips. In this role, he leads a team of over 400 software engineers and product managers to develop and deliver the remote and IoT capability for Philips devices. The team also provides innovative, data and software driven services to enable the digital transformation of Philips customers.

Before joining Philips, Stefan has served in several senior business and technology leadership roles at Siemens. He was head of strategy for the Digital Industries Software business, facilitating the expansion of portfolio and the transition to a SaaS business model, and head of the automotive industry practice, introducing solutions to tackle key challenges of the industry.

Stefan is a dual citizen of the US and Germany with broad international experience, born and raised in Germany and living in the US since 2001. He holds a PhD in Physics.

Rajiv Sivaraman

Rajiv in his current role as VP Global alliances with Siemens digital industry software business unit, Rajiv is involved with driving global strategic partnerships to help customers realize their digital enterprise aspirations. Rajiv comes with over 30 years’ cross Industry and cross-cultural experience that includes global and regional P&L leadership responsibilities across manufacturing, energy & infrastructure sectors and holds an honors degree in electrical & electronics engineering. In his tenure with Siemens, Rajiv has been instrumental in incubating startup business around Industrial IOT & Cyber security, built vertical market practice for data center vertical and developed the ISV ecosystem around the IOT platform MindSphere. Rajiv has also been a founding member & past director of the European association for Data Centers and is the chairman of executive committee for not-for-profit Industrial IoT business community (MindSphere world) in North America which he helped set up. Rajiv is part of the advisory board for the Georgia Institutes of Informatics’, University of Georgia.

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Prashant is Co-founder and COO at Pratiti Technologies. Throughout his career, his passion has been to help improve and transform business processes, by delivering NextGen products leveraging latest technologies. In the first 15 years, his focus was on the manufacturing industry and technologies such as 3D CAD/CAM. Since the last 10 years, his focus has been on applying Digital technologies such as cloud, IoT and AI in a wide range of industries such as healthcare, energy and manufacturing. Prashant is an alumnus of IIT Kharagpur and IIM Calcutta. He will be our moderator for the session today.

In his 25+ years of career, Nitin has played key roles in building software solutions right from ideation to deployment. As a Co-founder and CEO of Pratiti Labs Inc., he is committed to applying his management learning as well as the passion for building new solutions to realize end-client innovation with certainty. He is also the Co-founder and CEO of Helios IoT Systems – an energy analytics solution company that developed a patented digital twin, IoT and advanced analytics based solution – Apollo. He recently drove a strategic technical partnership between AVEVA and Helios IoT Systems and led Apollo solution to the ‘Next Gen Product of the Year’ recognition. Through his unique leadership style and innovation mindset, Nitin has also led Pratiti to several awards and recognitions from the partner ecosystem including ‘Siemens Partner of the Year’ as well as leading analyst organizations like Frost & Sullivan, Zinnov and NASSCOM. Through his many achievements in product engineering and leading two successful organizations, Nitin has gained impeccable and tangible skills in crafting and leading custom digital solutions to solve business problems across various industries.

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Girish is the founder of Shoonya and an expert in delivering Effective Business Solutions using Immersive Technologies. He has a Master’s degree in Computer Games Technology from the University of Abertay, Scotland.  After working in the Video Games industry for 5 years in Scotland, he came back to India in 2009 and founded Shoonya, with the aim of exploring applications of Immersive Technologies beyond Video Games. He has been through the evolution of Immersive Technologies like Augmented Reality & Virtual Reality right since their inception and has a deep understanding of how to effectively deliver AR/VR solutions that provide real business value. Over the past 13+ years, Girish has worked on several projects in industry sectors including Aviation, Transportation, Real Estate, Automobile, Energy, Telecom and Manufacturing, with clients across India, UK, Europe, Middle East and Singapore.

Ron Fritz is CEO and co-founder of Tech Soft 3D, the company that supplies underlying 3D technology to more than 700 companies developing engineering software, including Autodesk, Siemens, Dassault, PTC, Bentley, Trimble, ANSYS and many others. More recently, Tech Soft 3D has been providing technologies for CAD import to Digital Twin players including Unity, Unreal and nVidia Omniverse. Ron had led the company since it was founded in 1996 with a focus on business strategy and growth initiatives. The company is headquartered in Oregon in the United States with R&D offices in California, France and Norway.

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Deepak Konnur has 40+ years of experience in the Energy Utility business. Presently he is engaged with Adani Transmission as Advisor Digitalization helping technology and business teams to increase their productivity, predictability, reliability, efficiency through improved data and analytics backed informed decision making and to achieve enhanced returns on the assets deployed. He had worked as Chief Digital Officer, Tata power and was responsible for evangelizing and accelerating digitization initiatives & interventions across all entities of Tata Power. Prior to Joining Tata Power, Deepak was working with IBM as Vice President – Energy & Utilities Industry Solutions Deepak has long background in OT & IT Management and has developed and deployed OT & IT plans that achieve strategic business goals. Deepak is an Electrical Engineer and has done post graduation in Business Administration.

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Nitin is a postgraduate from IIT, Mumbai and in his 25 years of career, has played key roles in building software solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize customers’ innovation with certainty. Prior to co-founding Pratiti, Nitin was the Global COO for Geometric (now acquired by HCL).

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Prashant loves technology. While this passion helped him top his Masters class at IIT, he wanted to ensure his contribution remains ‘applied’ rather than ‘theoretical’. He is very excited by the convergence of technologies such as SMAC & IoT, and the Digital Transformation they are driving. In his current role, Prashant leads & manages key software projects at Pratiti to deliver higher value & strategic advantage to the clients.

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Stephen retired from Siemens in April 2020 as the CEO for the Siemens global IIoT business. Over the past 40 years, Stephen has always been grounded in a solid technical framework from hands on software troubleshooting to creating business outcomes from technology selections and business models.In his current role, he provides software advisory consulting through board positions, software projects and strategic software development practices. During his stint at Siemens, Stephen has received many accolades and is highly regarded for his contributions in MindSphere & Teamcenter.

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Study: minnesota's sex offender system is 'failed investment'.

Minnesota involuntarily commits more people for sex offenses than anywhere else in the nation per capita, but authors of a new report say the more than $100 million-a-year program fails to meaningfully address sexual violence or recognize the humanity of those it locks up.

Twenty states civilly commit sex offenders. Among those, Minnesota is "notorious" for the number of people it confines, the duration of their commitment and a low rate of community reintegration, according to the report released Wednesday by the Sex Offense Litigation and Policy Resource Center at Mitchell Hamline School of Law.

It recommends lawmakers sunset the program that holds more than 730 people and put the money toward community and victim support, sex violence prevention, resolving sex violence crimes and restorative practices.

"This is a very expensive intervention and doesn't have very much of an impact on sexual violence," said the center's Director Eric Janus, a longtime critic of the program. "Even among the 20 states that do this, we are doing it in a way that confines too many people for much too long. That's a civil rights issue, of course. But it's also a resource allocation issue."

The Minnesota Sex Offender Program (MSOP), which has facilities in Moose Lake and St. Peter, has faced years of backlash, primarily focused on civil rights concerns. Detainees sued in 2011, prompting a protracted legal battle over whether the system is constitutional. A U.S. District Court judge deemed it unconstitutional in 2015, but the decision was later reversed and the U.S. Supreme Court declined to hear a case challenging the system.

The program has continued to face legal challenges and a handful of detainees told the Star Tribune they feel long-standing issues still need to be addressed and many people there feel hopeless about their chances of ever leaving.

"MSOP follows the most current standards and best practices and is a recognized leader in sex-offense specific treatment. Public safety is at the heart of everything we do," the Department of Human Services, which operates the program, said in a statement. "The program is dedicated to providing the therapeutic guidance and tools clients need to break engrained patterns of behavior, make meaningful change and reduce their risk of re-offense."

The new report highlights problems raised in past lawsuits, including concerns about treatment, duration of confinement and delays. But unlike past legal challenges, it largely focuses on the program's cost and whether it's the best approach to address sexual violence.

Seeking consensus on closure

The program costs $479 a day per client, according to DHS . The state budgeted more than $110 million for MSOP in 2024.

Meanwhile, Minnesota spends about $2 million a year on other sex violence prevention efforts, such as grants to nonprofits doing sexual assault prevention services, the report found. It contends that Minnesota has fixated on "a tiny sliver" of the sexual violence problem — preventing repeat offenders — instead of spending state dollars on more comprehensive and effective efforts.

While the new study calls for an end to the state's civil commitment of sex offenders, advocates aren't pushing for changes in this year's legislative session, which concludes next month.

"We're interested in sunsetting this program. But we understand that would need to be done in a very careful, systematic, thoughtful way," Janus said, noting that broad consensus is essential to pave the way for legislative change.

Proposals to change MSOP have been "weaponized" in the past, he said. He noted there seemed to be bipartisan support for a task force's recommended changes more than a decade ago, but, "The minute that it looked like it was going to be a political issue it got dropped like a hot potato."

There is not consensus among members of the Minnesota Coalition Against Sexual Assault (MNCASA) about whether the state should sunset MSOP and reinvest the dollars, said Kate Hannaher, the organization's director of law and policy. However, she said advocates for victims of sexual violence were at the center of the creation of this report, which is not always the case.

"Whatever happens with the sex offender program, we need the money yesterday," Hannaher said, noting programs for sex violence survivors are struggling to stay open despite high demand.

MNCASA and Violence Free Minnesota, which works to combat relationship abuse, put out a joint statement on the Mitchell Hamline report, calling the disparity between state spending on MSOP and sexual violence prevention "alarming."

What is the future of MSOP?

The state created MSOP in the 1990s. Most people in the facilities have completed a prison sentence, then are civilly committed if a judge determines they have a "sexual psychopathic personality," are a "sexually dangerous person" or both.

The program fully discharged only one person in its first two decades of operation. That number has increased in recent years. As of March 28, the program had fully discharged 24 people. Others are on provisional discharge or are in a less-restrictive facility.

"Only a court has the authority to commit someone to MSOP or to discharge them," DHS noted in its response to the report. "Demonstrated participation and progress in treatment is the clear path to discharge. It is the most persuasive argument that clients can make when petitioning the court for a reduction in custody."

People are about five times more likely to die in MSOP facilities than be released, according to the Mitchell Hamline report. It blames the courts and MSOP's clinical leadership for one of the country's lowest discharge rates, noting that clinical staff influence transfer and discharge decisions.

"The harsh reality is that instead of making us safer, the state's attempts to predict future crime have created a new form of incarceration" that disproportionately confines people of color and targets LGBTQ community members, the report states. It highlights a 2013 Brooklyn Law Review study that concluded such civil commitment laws have "no discernible impact on the prevalence of sexual abuse."

About 95% of people convicted of criminal sexual conduct offenses in Minnesota did not have a prior sex offense, according to the most recent Minnesota Sentencing Guidelines Commission report .

"There's a scientific right way and an evidence-driven right way to deal with sexual violence and that's not what we're doing in Minnesota," said Daniel Wilson, who is civilly committed at the MSOP facility in Moose Lake after serving time for criminal sexual conduct against a child. He helped organize hunger strikes at MSOP in 2021 , during which detainees demanded the state offer a clear pathway for people to understand how they can be released.

Wilson is one of a handful of people locked up at Moose Lake who recently called the Star Tribune. Each shared similar concerns about limited access to treatment, a lack of quality and timely health care, seemingly subjective decisions about their futures and a widespread feeling of "hopelessness" that people will never get out.

"It's worse than what people can imagine, because they live in America," Wilson said. "We don't live in America in MSOP."

Jessie Van Berkel writes about Minnesota government and politics at the Star Tribune. She previously covered St. Paul City Hall and local government in the south metro.

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