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CS 410/510 Top: Mobile Health in the COVID Era
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CS 410/510 Top: Mobile Health in the COVID Era

Credit Hours:

4/3

Course Coordinator:

Nirupama Bulusu

Course Description:

The past few years have witnessed the emergence of mobile phone applications (Android health app) and wearables (e.g.. Fitbit, Jawbone, Nike Fuelband, Google Glasses, Microsoft Band, Apple Watch, etc.) as a platform for personalized health care., or what is referred to as mHealth. By leveraging state of the art computing technologies for advanced diagnostics, mHealth has long held the potential to radically revolutionize medicine, making it personalized, participatory, predictive and preventative. At the same time, the disruptions and threats to global health caused by the COVID-19 pandemic, have accelerated the need for adoption for mHealth tools for a wide range of applications. These include both the collective mHealth applications of contact tracing and exposure notification, enforcing social distancing and bio-bubbles, rapid testing and safe vaccine distribution, as well as individual mHealth applications such as remote monitoring and proactive intervention for high risk health groups such as the elderly, as well as patients suffering from chronic conditions such as cancer, hypertension, smoking, diabetes, obesity and depression.  This course will explore a range of issues related to implementing and realizing mHealth technologies, with a special emphasis on applications motivated by COVID-19.

Prerequisites:

None.

Goals:

Upon successful completion of this class, students will be able to:

  1. Describe how mHealth devices are being applied in medical settings.
  2. Distinguish point-of-care and wearable biosensors.
  3. Describe how mobile devices are being used for biological and behavior monitoring.
  4. Distinguish methods for understanding and changing patient behavior.
  5. Apply analytical models (statistics and machine learning) for extracting knowledge from mHealth data.
  6. Explain how human centered interfaces encompassing design, implementation and impacts of pedagogical agents can be applied to mHealth.
  7. Compare and contrast various technologies to monitor the security and privacy of mHealth data, particularly in the context of collective mHealth applications such as contact tracing and epidemic spread.
  8. Analyze health realities for resource-poor settings and the developing world and understand how to apply mHealth technologies to the developing world.
  9. Identify an mHealth problem and implement a prototype system to monitor the mHealth problem.

Textbooks:

There is no assigned textbook for this course. Lecture notes and lecture slides will be provided.

References:

[Adans-Dester20] C. P. Adans-Dester et al., "Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?," in IEEE Open Journal of Engineering in Medicine and Biology, vol. 1, pp. 243-248, 2020.

 

[Kumar13] Santosh Kumar, Wendy Nilsen, Misha Pavel, and Mani Srivastava. 2013. Mobile Health: Revolutionizing Healthcare Through Transdisciplinary Research. Computer 46, 1 (January 2013), 28-35.

 

[Estrin14a] “small data, where n = me”, Deborah Estrin, CACM, April 2014.

 

[Swan13] Swan, Melanie. "The quantified self: fundamental disruption in big data science and biological discovery." Big Data 1, no. 2 (2013): 85-99.

 

[Cohn12] Gabe Cohn, Sidhant Gupta, Tien-Jui Lee, Dan Morris, Joshua R. Smith, Matthew S. Reynolds, Desney S. Tan, and Shwetak N. Patel. 2012. An ultra-low-power human body motion sensor using static electric field sensing. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12). ACM, New York, NY, USA, 99-102.

 

[Cornelius14] Cory Cornelius, Ronald Peterson, Joseph Skinner, Ryan Halter, and David Kotz.

“A wearable system that knows who wears it”.In Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 55-67, June, 2014.

 

[Plarre11] Plarre, K.; Raij, A.; Hossain, S.M.; Ali, A.A.; Nakajima, M.; al'Absi, M.; Ertin, E.; Kamarck, T.; Kumar, S.; Scott, M.; Siewiorek, Daniel; Smailagic, A.; Wittmers, L.E., "Continuous inference of psychological stress from sensory measurements collected in the natural environment," Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on , vol., no., pp.97,108, 12-14 April 2011.

 

[SpiroSmart] Eric C. Larson, Mayank Goel, Gaetano Boriello, Sonya Heltshe, Margaret Rosenfeld, and Shwetak N. Patel. 2012. SpiroSmart: using a microphone to measure lung function on a mobile phone. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12). ACM, New York, NY, USA, 280-289.

 

[Noshadi12] Hyduke Noshadi, Foad Dabiri, Saro Meguerdichian, Miodrag Potkonjak, and Majid Sarrafzadeh. 2013. Behavior-oriented data resource management in medical sensing systems. ACM Trans. Sen. Netw. 9, 2, Article 12 (April 2013).

 

[StudentLife] Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell, StudentLife: Assessing Behavioral Trends, Mental Well-being and Academic Performance of College Students using Smartphones, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2014), September 2014.

 

[Bilicam] de Greef, Lilian, et al. "Bilicam: using mobile phones to monitor newborn jaundice." Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2014.

 

[Ramanathan12] Nithya Ramanathan, Faisal Alquaddoomi, Hossein Falaki, Dony George, Cheng-Kang Hsieh, John Jenkins, Cameron Ketcham, Brent Longstaff, Jeroen Ooms, Joshua Selsky, Hongsuda Tangmunarunkit, Deborah Estrin, “ohmage: An Open Mobile System for Activity and Experience Sampling”, In Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare. May 21-24th, 2012.

 

[Kellogg14] Bryce Kellogg, Vamsi Talla, and Shyamnath Gollakota. 2014. Bringing gesture recognition to all devices. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation (NSDI'14). USENIX Association, Berkeley, CA, USA, 303-316.

 

[Rehg14] Rehg, James M., Agata Rozga, Gregory D. Abowd, and Matthew S. Goodwin. "Behavioral imaging and autism." Pervasive Computing, IEEE 13, no. 2 (2014): 84-87.

 

[Swartout13] Swartout, W., Artstein, R., Forbell, E., Foutz, S., Lane, H.C., Lange, B., Morie, J., Noren, D., Rizzo, S., & Traum, D. (2013). Virtual Humans for Learning. In V. Chaudhri, D. Gunning, H.C. Lane, & J. Roschelle (Eds) AI Magazine, Special issue on Intelligent Learning Technologies, 34(4), 13-30.

 

[Gullapalli19] Bhanu Teja Gullapalli, Annamalai Natarajan, Gustavo A. Angarita, Robert T. Malison, Deepak Ganesan, Tauhidur Rahman, On-body Sensing of Cocaine Craving, Euphoria and Drug-Seeking Behavior using Cardiac and Respiratory Signals, Proceedings of ACM Interact. Mob. Wearable Ubiquitous Technol. (Ubicomp 2019).

 

[Parate17] Abhinav Parate, Deepak Ganesan, Detecting Eating and Smoking Behaviors Using Smartwatches, Mobile Health, Springer Publications 2017

 

[Mavandadi2012] Sam Mavandadi, Steve Feng, Frank Yu, Stoyan Dimitrev, Richard Yu, Aydogan Ozcan, “BioGames: A Platform for Crowd-Sourced BioMedical Image Analysis and Telediagnosis", GAMES FOR HEALTH JOURNAL: Research, Development, and Clinical Applications Volume 1, Number 5, 2012.

 

[Musthag11] Mohamed Musthag, Andrew Raij, Deepak Ganesan, Santosh Kumar, and Saul Shiffman. 2011. Exploring micro-incentive strategies for participant compensation in high- burden studies. In Proceedings of the 13th international conference on Ubiquitous computing (UbiComp '11).

 

[Crane11] Crane, Johanna. "Scrambling for Africa? Universities and global health." The Lancet 377.9775 (2011): 1388-1390.

 

[Quinn14] Quinn, John, Vanessa Friar-Martinez, and Lakshminarayan Subramanian. "Computational Sustainability and Artificial Intelligence in the Developing World." AI Magazine Special Issue on Computational Sustainability (2014).

 

[Curioso10] Walter H. Curioso and Patricia N. Mechael Enhancing 'M-Health' With South-To-South Collaborations Health Affairs, 29, no.2 (2010): 264-267.

 

[Marcus09] Adam Marcus, Guido Davidzon, Denise Law, Namrata Verma, Rich Fletcher, Aamir Khan, and Luis Sarmenta. 2009. Using NFC-Enabled Mobile Phones for Public Health in Developing Countries. In Proceedings of the 2009 First International Workshop on Near Field Communication (NFC '09). IEEE Computer Society, Washington, DC, USA, 30-35.

 

[Ming20] Ming L, Untong N, Aliudin N, Osili N, Kifli N, Tan C, Goh K, Ng P, Al-Worafi Y, Lee K, Goh H

Mobile Health Apps on COVID-19 Launched in the Early Days of the Pandemic: Content Analysis and Review

JMIR Mhealth Uhealth 2020;8(9):e19796

 

[Raij11] Andrew Raij, Animikh Ghosh, Santosh Kumar, and Mani Srivastava. 2011. Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11). ACM, New York, NY, USA, 11-20.

 

[Rushanan14] Michael Rushanan, Aviel D. Rubin, Denis Foo Kune, and Colleen M. Swanson. 2014. SoK: Security and Privacy in Implantable Medical Devices and Body Area Networks. In Proceedings of the 2014 IEEE Symposium on Security and Privacy (SP '14). IEEE Computer Society, Washington, DC, USA, 524-539.

 

[Gollakotta11] Shyamnath Gollakota, Haitham Hassanieh, Benjamin Ransford, Dina Katabi, and Kevin Fu. 2011. They can hear your heartbeats: non-invasive security for implantable medical devices. In Proceedings of the ACM SIGCOMM 2011 conference (SIGCOMM '11). ACM, New York, NY, USA, 2-13.

 

[Salathe20] Salathé M, Althaus C, Anderegg N, Antonioli D, Ballouz T, Bugnon E, Čapkun S, Jackson D, Kim SI, Larus J, Low N, Lueks W, Menges D, Moullet C, Payer M, Riou J, Stadler T, Troncoso C, Vayena E, von Wyl V. Early evidence of effectiveness of digital contact tracing for SARS-CoV-2 in Switzerland. Swiss Med Wkly. 2020 Dec 16;150:w20457.

 

[Troncoso20] Troncoso, C. et al. “Decentralized Privacy-Preserving Proximity Tracing.” IEEE Data Eng. Bull. 43 (2020): 36-66.

Major Topics:

Social and Ethical Issues:

We will explore social and ethical issues both in the broad context of mHealth, and more specifically in

terms of applications like contact tracing and exposure notification.