Award Abstract # 1231577
SHB: Type I (EXP): Collaborative Research: EasySense: Contact-less Physiological Sensing in the Mobile Environment Using Compressive Radio Frequency Probes

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: OHIO STATE UNIVERSITY, THE
Initial Amendment Date: September 14, 2012
Latest Amendment Date: September 14, 2012
Award Number: 1231577
Award Instrument: Standard Grant
Program Manager: Sylvia Spengler
sspengle@nsf.gov
 (703)292-7347
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2012
End Date: September 30, 2016 (Estimated)
Total Intended Award Amount: $240,000.00
Total Awarded Amount to Date: $240,000.00
Funds Obligated to Date: FY 2012 = $240,000.00
History of Investigator:
  • Emre Ertin (Principal Investigator)
    ertin.1@osu.edu
Recipient Sponsored Research Office: Ohio State University
1960 KENNY RD
COLUMBUS
OH  US  43210-1016
(614)688-8735
Sponsor Congressional District: 03
Primary Place of Performance: Ohio State University
2015 Neil Avenue
Columbus
OH  US  43210-1210
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): DLWBSLWAJWR1
Parent UEI: MN4MDDMN8529
NSF Program(s): Smart and Connected Health
Primary Program Source: 01001213DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8018, 8061
Program Element Code(s): 801800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The collaborative research project (IIS-1231754, Santosh Kumar, University of Memphis; IIS-1231525, Mustafa al'Absi, University of Minnesota Twin Cities; IIS-1231577, Emre Ertin, Ohio State University) is developing and evaluating a mobile sensor called EasySense that can provide continuous physiological monitoring without skin contact in the field environments using radio frequency (RF) probes. This approach addresses the problem of physiological monitoring today that requires skin contacts such as electrodes for ECG, and hence cannot scale to widespread monitoring of patients and healthy adults for years. The key challenge is to develop high-resolution sensing on low-power mobile platforms that can separate out the weak motion signals of heart and lung, from the gross motion of the body and the sensor. The project is developing theory and design for a compressive ultrawideband (UWB) RF sensor that achieves two orders of magnitude reduction in the required sampling rate to make it feasible to realize in a low-power mobile form factor. EasySense employs dynamic compressive sensing algorithms to improve the quality of sensing through temporal integration of information and employs interference subspace cancelation methods to cancel out motion artifacts using data obtained from accelerometers and gyroscopes. The project is implementing all the needed hardware, firmware, embedded software on the sensor node for sampling, processing, and wireless communication, and mobile phone software for data collection, storage, and visualization. EasySense is evaluated against traditional physiological sensors via lab and field studies on human subjects involving stress and exercise protocols.

By realizing contactless sensing of physiology in the field environment, EasySense will enable long-term physiological monitoring at large-scale that is essential for determining potential causes and early biomarkers of fatal diseases of slow accumulation such as cancer and cardiovascular diseases. In addition to being used widely in health research and practice, EasySense can be used for hands-on demonstration in health education. Information on the project, developed hardware and software design files and code relating to the testbed infrastructure will be accessible in open source form via the project web site (http://www.easysense.org).

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chatterjee, Soujanya and Hovsepian, Karen and Sarker, Hillol and Saleheen, Nazir and al'Absi, Mustafa and Atluri, Gowtham and Ertin, Emre and Lam, Cho and Lemieux, Andrine and Nakajima, Motohiro and Spring, Bonnie and Wetter, David W. and Kumar, Santosh "mCrave: Continuous Estimation of Craving During Smoking Cessation" Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing , 2016 , p.863--874 10.1145/2971648.2971672
J. Gao, D. Teng, and E. Ertin "ECG feature detection using randomly compressed samples for stable HRV analysis over low rate links" Proceedings of the IEEE International Conference on Wearable and Implantable Body Sensor Networks , 2016 , p.165-170 10.1109/BSN.2016.7516253
K. Hovsepian, M. al?Absi, E. Ertin, T. Kamarck, M. Nakajima, and S. Kumar "cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment" ACM UbiComp , 2015
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

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Long-term monitoring of physiology at large-scale can help determine potential causes and early biomarkers of fatal diseases of slow accumulation such as cancer and heart diseases that are major causes of mortality. Physiological monitoring today, however, requires sensors attached to the body surface such as electrodes for ECG and EMG. The burden associated with the use of wearable sensors in daily life especially for long-term usage is a major roadblock to the widespread adoption of mobile health, especially among patients. This project's goal is to develop and evaluate a mobile device (dubbed as EasySense) that can provide physiological measurements without contact with the skin in both lab and field environments. 

Key outcomes of the project are:

1. A fully functioning prototype  all digital, multi input-multi output (MIMO), Ultrawideband (UWB) radar in mobile form factor, operating in 0.5-3.5 GHz band.

2. Design of compressive sampling schemes for making MIMO measurements of the body and associated subspace learning and exploitation techniques for estimation of physiological signals related to heart and lung motion.

3. Validation against traditional ECG and respiratory inductance plethysmography sensors in the lab setting.

 

The developed UWB sensor provides a means to collect rich data set for RF probing of the physiological processes. The developed interference estimation and suppression algorithms are applicable to many radar applications where weak signals of interest to be extracted in the presence of large signals from background sources and clutter. 

 

Continuous sensing of physiology in the field for long-term can provide visibility into the etiology of complex human diseases. Therefore this project has the potential to bring revolutionary changes in improving research and practice in healthcare. Contactless monitoring of physiological signals in mobile environment can assist psychosocial researchers to conduct large public health studies. Eventual use of EasySense for continuous monitoring of cardiovascular health can help individuals obtain early warning of imminent heart problems that causes sudden deaths.

 

 

This is a collaborative project with University of Memphis and University of Minnesota, where human subject validation phase of the project is in progress. Our joint effort is towards development  into mathematical models for automated detection of stress, craving, smoking, conversation, drug use from physiological measurements.  Each of these models provides new capabilities using which new mobile health systems can be designed to monitor and improve health. Modeling techniques discovered in the process are also directly reusable in automated detection of other behaviors and health states such as eating. 

 


Last Modified: 01/30/2017
Modified by: Emre Ertin

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