How Is grandma doing? predicting functional health status from binary ambient sensor data

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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Abstract

Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
Original languageEnglish
Title of host publicationArtificial intelligence for gerontechnology
Subtitle of host publicationpapers from the AAAI symposium
EditorsDiane J. Cook, Narayanan C. Krishnan, Parisa Rashidi, Marjorie Skubic, Alex Mihailidis
PublisherAAAI Press
Pages26-31
ISBN (Print)9781577355908
Publication statusPublished - 2012
EventAAAI Symposium - Arlington, Virginia, United States
Duration: 2 Nov 20124 Nov 2012

Publication series

NameAAAI Technical Report
PublisherAAAI Press
NumberFS-12-01

Conference

ConferenceAAAI Symposium
CountryUnited States
CityArlington, Virginia
Period2/11/124/11/12

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