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dc.contributor.authorHsu, Chen-Yu
dc.contributor.authorLiu, Yuchen
dc.contributor.authorKabelac, Zachary
dc.contributor.authorHristov, Rumen
dc.contributor.authorKatabi, Dina
dc.contributor.authorLiu, Christine
dc.date.accessioned2021-11-08T17:44:51Z
dc.date.available2021-11-08T17:44:51Z
dc.date.issued2017-05-02
dc.identifier.urihttps://hdl.handle.net/1721.1/137729
dc.description.abstract© 2017 ACM. Gait velocity and stride length are critical health indicators for older adults. A decade of medical research shows that they provide a predictor of future falls, hospitalization, and functional decline among seniors. However, currently these metrics are measured only occasionally during medical visits. Such infrequent measurements hamper the opportunity to detect changes and intervene early in the impairment process. In this paper, we develop a sensor that uses radio signals to continuously measure gait velocity and stride length at home. Our sensor hangs on a wall like a picture frame. It does not require the monitored person to wear or carry a device on her body. Our approach builds on recent advances in wireless systems which have shown that one can locate people based on how their bodies impact the surrounding radio signals. We demonstrate the accuracy of our method by comparing it to the gold standard in clinical tests, and the VICON motion tracking system. Our experience from deploying the sensor in 14 homes indicates comfort with the technology and a high acceptance rate.en_US
dc.language.isoen
dc.publisherACMen_US
dc.relation.isversionof10.1145/3025453.3025937en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleExtracting Gait Velocity and Stride Length from Surrounding Radio Signalsen_US
dc.typeArticleen_US
dc.identifier.citationHsu, Chen-Yu, Liu, Yuchen, Kabelac, Zachary, Hristov, Rumen, Katabi, Dina et al. 2017. "Extracting Gait Velocity and Stride Length from Surrounding Radio Signals."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-06-06T17:56:57Z
dspace.date.submission2019-06-06T17:56:59Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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