
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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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: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1960 KENNY RD COLUMBUS OH US 43210-1016 (614)688-8735 |
Sponsor Congressional District: |
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Primary Place of Performance: |
2015 Neil Avenue Columbus OH US 43210-1210 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Smart and Connected Health |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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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).
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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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