mHealth Sensor Platforms: With support from NIH’s
Genes Environment & Health Initiative (GEI), my team developed the AutoSense sensor suite [1] that hosts ten sensors
(ECG, respiration, skin conductance, accelerometry, temperature, alcohol,
etc.), is ultra-low-power, is optimized for on-body sensing in the mobile
environment, and convenient for long-term wearing. AutoSense is
complemented by a robust software framework on the mobile phone that collects continuous
measurements from wearable wireless sensors, processes them to make health
inferences, and solicits self-reports on the phone, all in real-time. This system has been worn by 100+
human volunteers (including daily smokers, drinkers, and drug users) for 20,000+
hours in their natural environments as part of various field studies [2,3]. Due
to its real-life successes, AutoSense has been featured in congressional
reports from NIH.
As part of an NSF
Smart Health project called EasySense, we are developing a contactless
physiological sensor that uses radio
frequency (RF) probes to track movements of heart and lungs without any skin
contact [4]. This breakthrough advancement is accomplished by using Doppler
sensing, but with ultra-wideband RF probes, and use of radar techniques and
compressive sensing to realize a low-power receiver that can fit on a tiny
mobile device. RF sensing also makes it possible to estimate fluid build-up in the lungs that precedes heart failure in
congestive heart failure (CHF) patients, providing the first-ever opportunity to non-invasively
monitor worsening of lung congestion.
- Ertin,
E., Stohs, N., Kumar, S., Raij, A.B., al'Absi, M., Kwon, T., Mitra, S., Shah,
S., and Jeong, J.W. (2011). AutoSense: Unobtrusively Wearable Sensor Suite for
Inferencing of Onset, Causality, and Consequences of Stress in the Field. In
Proceedings of ACM SenSys. (14 pages).
-
Rahman,
M., Bari, R., Ali, A.A., Sharmin, M., Raij, A., Hovsepian, K., Hossain, M.,
Ertin, E., Kennedy, A., Epstein, D., Preston, K., Jobes, M., Kedia, S., Ward,
K., al’Absi, M., and Kumar, S. (2014). Are We There Yet? Feasibility of
Continuous Stress Assessment via Wireless Physiological Sensors. In Proceedings
of ACM Conference on Bioinformatics,
Computational Biology, and Health Informatics (BCB), pp. 479-488
-
Kennedy,
A.P., Epstein, D. H., Jobes, M. L., Agage, D., Tyburski, M., Phillips, K., Ali,
A., Bari, R., Hossain, S.M., Hovsepian, K., Rahman, M., Ertin, E., Kumar, S.,
and Preston, K. (Accepted). Continuous In-The-Field Measurement of Heart Rate:
Correlates of Drug Use, Craving, Stress, and Mood in Polydrug Users. Drug and Alcohol Dependence. (27 pages).
-
Gao,
J., Ertin, E., Kumar, S., and al’Absi, M. (2013). Contactless Sensing of
Physiological Signals Using Wideband RF Probes. Asilomar Conference on Signals, Systems, and Computers, pp. 86-90.
Sensor-based mHealth Markers: The promise of mHealth is to provide unprecedented visibility into the
physical, physiological, psychological, social, and environment state of an
individual so as to discover the causes of various diseases which can be used
in the development of treatments, interventions, and prevention programs. With support from NSF,
we have used the real-life sensor data collected from AutoSense to develop computationally
models for sensor-based continuous monitoring of stress [1,2], conversations [3],
smoking events [4,5], and cocaine use events [6] from sensor data. These works
have established the wide utility of physiological monitoring for automated
detection of human behaviors in the field and enabled identification of
sensor-based predictors of adverse health events, which can be used to realize
sensor-based just-in-time interventions.
-
K.
Hovsepian, M. al’Absi, E. Ertin, T. Kamarck,
and S. Kumar. cStress: Towards a Gold Standard for Continuous Stress
Assessment
in the Mobile Environment, ACM UbiComp 2015.(12
pages)
- Plarre, K., Raij,
A.B., Hossain, M., Ali, A.A., Nakajima, M., al’Absi, M., Ertin, E.,
Kamarck, T., Kumar, S., Scott, M., Siewiorek, D., Smailagic, A., and
Wittmers, L. (2011). Continuous Inference of Psychological Stress from
Sensory Measurements Collected in the Natural Environment. In Proceedings
of ACM IPSN. (12 pages) (Nominated for Best Paper Award)
- Rahman, M., Ali,
A.A., Plarre, K., al'Absi, M., Ertin, E., and Kumar, S. (2011). mConverse:
Inferring Conversation Episodes from Respiratory Measurements Collected in
the Field. In Proceedings of ACM
Wireless Health.(10 pages) (Nominated for
Best Paper Award)
-
N.
Saleheen, A. A. Ali, S. M. Hossain, H.
Sarker, S. Chatterjee, B. Marlin, E. Ertin, M. al’Absi, and S. Kumar.
puffMarker: A Multi-sensor Approach for Pinpointing the Timing of First
Lapse
in Smoking Cessation, ACM UbiComp 2015. (12 pages)
- Ali, A.A.,
Hossain, M., Hovsepian, L., Rahman, M., Plarre K., and Kumar, (2012).
mPuff: Automated Detection of Cigarette Smoking Puffs from Respiration
Measurements. In Proceedings of ACM
IPSN. (12 pages)
- Hossain, M., Ali,
A.A., Ertin, E., Epstein, D., Preston, K., Umbricht, A., Chen, Y., and
Kumar, S. (2014). Identifying Drug (Cocaine) Intake Events from Acute
Physiological Response in the Presence of Free-living Physical Activity.
In Proceedings of ACM IPSN. (12
pages)
Sensor-Triggered Just-in-Time mHealth
Interventions:
The sensing and inferencing of human states becomes
practically useful when used in the design, development, and delivery of an
intervention to improve health. As a first step towards development of
sensor-triggered just-in-time interventions, we have developed methods to
determine (from sensor data) when a user may be physically, cognitively, and
socially available to be engaged in an intervention [1]. We
find that users are
least available at work and during driving, and most available when
walking
outside. Subsequently, we have identified from continuous measurement
of stress
in the natural environment that users are most stressed during driving.
By
using our continuous measurement of stress and detection of
instantaneous driving
events from GPS (e.g., braking), we have identified major factors that
cause
stress during driving [2]. We have also developed visualizations for
continuous time series of stress data to help develop just-in-time
interventions [3]. Going forward, to identify sensor-based predictors of smoking lapse, we are conducting a
field study on 75 newly abstinent smokers (as part of an R01 from the OppNet
initiative at NIH) who are wearing AutoSense before and after quitting smoking.
With continuous monitoring of stress, conversation, smoking, drinking, craving,
and lapse events, we plan to identify vulnerable moments that precipitate lapse
in a newly abstinent smoker. Automated detection of these moments on the mobile
phone will then be used to determine when to deliver a just-in-time
intervention on a mobile phone to prevent lapse in newly abstinent smokers.
- Sarker,
H., Sharmin, M., Ali, A.A., Rahman, M., Bari, R., Hossain, M., and Kumar, S.
(2014). Assessing the Availability of Users to Engage in Just-in-Time
Intervention in the Natural Environment. In Proceedings of ACM UbiComp. (12 pages) #
Cited: 3
- Vhaduri,
S., Ali, A.A., Sharmin, M., Hovsepian, K., and Kumar, S. (2014). Estimating
Drivers' Stress from GPS Traces. In Proceedings of Automotive UI, pp. 1-8.
-
M.
Sharmin, A. Raij, D. Epstein, I. Nahum-Shani,
J. G. Beck, S. Vhaduri, K. Preston, and S. Kumar. Visualization of
Time-Series
Sensor Data to Inform the Design of Just-In-Time Adaptive Stress
Interventions,
ACM UbiComp 2015. (12 pages)
mHealth Privacy: mHealth systems have deep privacy implications. Our work on mHealth
inferencing in the FieldStream project revealed that sensors once considered
innocuous, such as respiration, can reveal
potentially private behaviors and psychological states such as stress, conversation,
smoking, or drug use events. This raises new privacy issues for sharing of
mHealth data [1], since the focus of privacy research has traditionally been
on protecting the identity of individuals in a group, and not on protecting the
revelation of private behaviors. As part of a new CSR project from NSF (led by
UCLA, PI: Mani Srivastava), we are working towards novel approaches to share
mHealth data that prevents the revelation of undesired behaviors while
permitting the inferences of desired health conditions.
- Raij, A.B., Ghosh,
A., Kumar, S., Srivastava, M.B. (2011). Privacy risks emerging from the
adoption of innocuous wearable sensors in the mobile environment. In
Proceedings of ACM CHI. (10
pages)
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Post
Doctoral Fellows
Dr.
Moushumi Sharmin (2013-15) - Joined
Western Washington University in Sep 2015 as Tenure-track Assistant
Professor
Dr.
Andrew Raij (2009-10) - Joined
University of South Florida in 2011 as Tenure-track Assistant
Professor
Dr. Karen Hovsepian
(2011-12) - Joined
Troy University as Tenure-track Assistant Professor
Ph.D. Students
Mahbubur Rahman
(2009-) -
mmrahman(dot)(at)memphis(dot)edu
Syed Monowar Hussain (2010-)
Hillol Sarker (2012-)
Nazir Saleheen (2013-)
Rummana
Bari (2013-)
Soujanya
Chaterjee (2014-)
M.S. Students
Nusrat Nasreen (2013-)
Undergraduate
Students
Daniel Lissner (2009-2010)
Vineet Sathyan (2008-2009)
Alumni
Amin Ahsan Ali
(Ph.D., 2014) - Joined University of Dhaka as Assistant Professor
Somnath
Mitra (M.S., 2012) - eBay
Animikh
Ghosh (M.S., 2010) - GE Global Research, India
Salahuddin
Mohammad Masum (2010) - Ph.D.
Student, EECE,
University of Memphis
Maheshbabu Satharla (M.S.,
2010)
Bhagavathy Krishna (M.S.,
2009)
Tim Henry (B.S., 2008) -
Fedex, Memphis
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