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Automated Health Assessment through Mobile Sensing and Machine Learning of Daily Activities

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41EB029774-01A1
Agency Tracking Number: R41EB029774
Amount: $347,739.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NIBIB
Solicitation Number: PAR18-326
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-09-20
Award End Date (Contract End Date): 2020-08-31
Small Business Information
525 NW ASPEN CT, Pullman, WA, 99163-5387
DUNS: 079954783
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 LAWRENCE HOLDER
 (509) 432-1812
 larry@adaptelligence.com
Business Contact
 LAWRENCE HOLDER
Phone: (509) 432-1812
Email: larry@adaptelligence.com
Research Institution
 WASHINGTON STATE UNIVERSITY
 LIGHTY 280, PO BOX 641060
PULLMAN, WA, 99164-1060
 Nonprofit college or university
Abstract
PROJECT SUMMARY ABSTRACTAdvances in health care have been dramatic since the beginning of the millenniumAs a resultpeople are living longer with age related diseasesand the number of older individuals unable to live independently is rising rapidlyMobile computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to automate analysis of functional health in a personandapos s everyday settingsThis project focuses on evaluating the performance and commercial viability of technologies that will meet some of the needs that this coming age wave introduces by automating assessment of a personandapos s functional performanceThe primary objective of this Phase I STTR application is to evaluate the feasibility of assessing an individualandapos s cognitive and mobility based health using behavior patterns as sensed by a smart watchAchieving this objective will provide a foundation for the Phase II application goal of using multiple information sources to automatically generate activity scores and functional health measures from sensor dataBuilding on our prior collaborative workour approach creates a profile of a personandapos s routine behavior through automated real time recognition of complex activities from mobile sensor dataAimWe will evaluate the use of machine learning techniques to map behavior features onto cognitive and mobility health scores provided through app and in person neuropsychological assessmentAimFinallywe will evaluate an interactive visual tool for displaying behavior patterns to provide individuals and their caregivers with insights on their routines and relationship with their health statusAimThis work has important health care implications as functional impairment has been associated with negative outcomes including increased health care utilizationfallsand conversion to dementiaGiven nursing home care coststhe impact of family based careand the importance that people place on staying at homeit is imperative to commercialize technologies that increase functional independence while improving quality of life for both individuals and their caregivers PROJECT NARRATIVE We propose to evaluate the performance and commercial viability of an application that can predict an individualandapos s cognitive and mobility based health based on patterns that are sensed by a smart watchand effectively and efficiently present this information to the caregiverThis work will lay the foundation for a commercial tool to analyze behavior routines and automate assessment of functional healthThis research is relevant to public health because these technologies can extend the functional independence of our aging society through technology assisted health self managementreduce caregiver burdenand improve quality of life

* Information listed above is at the time of submission. *

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