Extracting Gait Velocity and Stride Length from Surrounding Radio Signals
© 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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM
2021
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| Online Access: | https://hdl.handle.net/1721.1/137729 |
| _version_ | 1811092079070150656 |
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| author | Hsu, Chen-Yu Liu, Yuchen Kabelac, Zachary Hristov, Rumen Katabi, Dina Liu, Christine |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hsu, Chen-Yu Liu, Yuchen Kabelac, Zachary Hristov, Rumen Katabi, Dina Liu, Christine |
| author_sort | Hsu, Chen-Yu |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T15:12:39Z |
| format | Article |
| id | mit-1721.1/137729 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:12:39Z |
| publishDate | 2021 |
| publisher | ACM |
| record_format | dspace |
| spelling | mit-1721.1/1377292023-02-10T19:48:33Z Extracting Gait Velocity and Stride Length from Surrounding Radio Signals Hsu, Chen-Yu Liu, Yuchen Kabelac, Zachary Hristov, Rumen Katabi, Dina Liu, Christine Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 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. 2021-11-08T17:44:51Z 2021-11-08T17:44:51Z 2017-05-02 2019-06-06T17:56:57Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137729 Hsu, Chen-Yu, Liu, Yuchen, Kabelac, Zachary, Hristov, Rumen, Katabi, Dina et al. 2017. "Extracting Gait Velocity and Stride Length from Surrounding Radio Signals." en 10.1145/3025453.3025937 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ACM MIT web domain |
| spellingShingle | Hsu, Chen-Yu Liu, Yuchen Kabelac, Zachary Hristov, Rumen Katabi, Dina Liu, Christine Extracting Gait Velocity and Stride Length from Surrounding Radio Signals |
| title | Extracting Gait Velocity and Stride Length from Surrounding Radio Signals |
| title_full | Extracting Gait Velocity and Stride Length from Surrounding Radio Signals |
| title_fullStr | Extracting Gait Velocity and Stride Length from Surrounding Radio Signals |
| title_full_unstemmed | Extracting Gait Velocity and Stride Length from Surrounding Radio Signals |
| title_short | Extracting Gait Velocity and Stride Length from Surrounding Radio Signals |
| title_sort | extracting gait velocity and stride length from surrounding radio signals |
| url | https://hdl.handle.net/1721.1/137729 |
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