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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF PITTSBURGH THE
Doing Business As Name:University of Pittsburgh
PD/PI:
  • Mark S Redfern
  • (412) 647-7923
  • mredfern@pitt.edu
Award Date:09/05/2009
Estimated Total Award Amount: $ 290,092
Funds Obligated to Date: $ 290,092
  • FY 2009=$290,092
Start Date:09/01/2009
End Date:08/31/2012
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040101 RRA RECOVERY ACT
Award Title or Description:CPS:Medium:Collaborative Research:Monitoring Human Performance with Wearable Accelerometers
Federal Award ID Number:0931595
DUNS ID:004514360
Parent DUNS ID:004514360
Program:CPS-Cyber-Physical Systems
Program Officer:
  • Sylvia Spengler
  • (703) 292-8930
  • sspengle@nsf.gov

Awardee Location

Street:300 Murdoch Building
City:Pittsburgh
State:PA
ZIP:15260-3203
County:
Country:US
Awardee Cong. District:18

Primary Place of Performance

Organization Name:University of Pittsburgh
Street:300 Murdoch Building
City:Pittsburgh
State:PA
ZIP:15260-3203
Country:US
Cong. District:18

Abstract at Time of Award

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The objective of this research is to develop a cyber-physical system composed of accelerometers and novel machine learning algorithms to analyze data in the context of a set of driving health care applications. The approach is to develop novel machine learning algorithms for temporal segmentation, classification, and detection of subtle elements of human motion. These techniques will allow quantification of human motion and improved full-time monitoring and assessment of medical conditions using a lightweight wearable system. The scientific contribution of this research is in advancing machine learning and human sensing in support of improved medical diagnoses and treatment monitoring by (i) modeling human activity and symptoms through sensor data analysis, (ii) integrating and fusing information from several accelerometers to monitor in real-time, (iii) validating the efficacy of the automated detection through assessments applying the state of the art in diagnostic evaluation, (iv) developing novel machine learning methods for temporal segmentation, classification, and discovery of multiple temporal patterns that discriminate between temporal signals, and (v) providing quality measures to characterize subtle human motion. These algorithms will advance machine learning in the area of unsupervised and semisupervised learning. The driving applications for this research are job coaching for people with cognitive disabilities, tele-rehabilitation for knee osteo-arthritis, assessing variability in balance and gait as an indicator of health of older adults, and measures for assessing Parkinson's patients. This research is highly interdisciplinary and will train graduate students for careers in developing technological innovations in health and monitoring systems.

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