Elderly care: activities of daily living classification with an S band radar
Falls in the elderly represent a serious challenge for the global population. To address it, monitoring of daily living has been suggested, with radar emerging to be a useful platform for it due to its various benefits with acceptance and privacy. Here, we show results from the use of an S band rada...
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doaj-bbdca57f1b324ae18e96abdb18e52f9d2021-04-02T12:48:38ZengWileyThe Journal of Engineering2051-33052019-10-0110.1049/joe.2019.0561JOE.2019.0561Elderly care: activities of daily living classification with an S band radarAman Shrestha0Julien Le Kernec1Francesco Fioranelli2Francesco Fioranelli3Yier Lin4Qian He5Jordane Lorandel6Olivier Romain7Communication, Sensing and Imaging group, School of Engineering, University of GlasgowCommunication, Sensing and Imaging group, School of Engineering, University of GlasgowCommunication, Sensing and Imaging group, School of Engineering, University of GlasgowCommunication, Sensing and Imaging group, School of Engineering, University of GlasgowSchool of Information and Electronics, University of Electronic Science and Technology of ChinaSchool of Information and Electronics, University of Electronic Science and Technology of ChinaUniversité Cergy-PontoiseUniversité Cergy-PontoiseFalls in the elderly represent a serious challenge for the global population. To address it, monitoring of daily living has been suggested, with radar emerging to be a useful platform for it due to its various benefits with acceptance and privacy. Here, we show results from the use of an S band radar for activity detection and the importance of selecting specific frequency bins to improve its suitability for human movement classification. The use of feature selection to improve detection of key activities such as falls has been presented. Initial results of 65% are improved to 85% and further to 90% with the aforementioned methods.https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0561geriatricspattern classificationdata privacyhealth careradarfeature selectionfeature selectionelderly caredaily living classificationprivacyactivity detectionhuman movement classifications band radar |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aman Shrestha Julien Le Kernec Francesco Fioranelli Francesco Fioranelli Yier Lin Qian He Jordane Lorandel Olivier Romain |
spellingShingle |
Aman Shrestha Julien Le Kernec Francesco Fioranelli Francesco Fioranelli Yier Lin Qian He Jordane Lorandel Olivier Romain Elderly care: activities of daily living classification with an S band radar The Journal of Engineering geriatrics pattern classification data privacy health care radar feature selection feature selection elderly care daily living classification privacy activity detection human movement classification s band radar |
author_facet |
Aman Shrestha Julien Le Kernec Francesco Fioranelli Francesco Fioranelli Yier Lin Qian He Jordane Lorandel Olivier Romain |
author_sort |
Aman Shrestha |
title |
Elderly care: activities of daily living classification with an S band radar |
title_short |
Elderly care: activities of daily living classification with an S band radar |
title_full |
Elderly care: activities of daily living classification with an S band radar |
title_fullStr |
Elderly care: activities of daily living classification with an S band radar |
title_full_unstemmed |
Elderly care: activities of daily living classification with an S band radar |
title_sort |
elderly care: activities of daily living classification with an s band radar |
publisher |
Wiley |
series |
The Journal of Engineering |
issn |
2051-3305 |
publishDate |
2019-10-01 |
description |
Falls in the elderly represent a serious challenge for the global population. To address it, monitoring of daily living has been suggested, with radar emerging to be a useful platform for it due to its various benefits with acceptance and privacy. Here, we show results from the use of an S band radar for activity detection and the importance of selecting specific frequency bins to improve its suitability for human movement classification. The use of feature selection to improve detection of key activities such as falls has been presented. Initial results of 65% are improved to 85% and further to 90% with the aforementioned methods. |
topic |
geriatrics pattern classification data privacy health care radar feature selection feature selection elderly care daily living classification privacy activity detection human movement classification s band radar |
url |
https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0561 |
work_keys_str_mv |
AT amanshrestha elderlycareactivitiesofdailylivingclassificationwithansbandradar AT julienlekernec elderlycareactivitiesofdailylivingclassificationwithansbandradar AT francescofioranelli elderlycareactivitiesofdailylivingclassificationwithansbandradar AT francescofioranelli elderlycareactivitiesofdailylivingclassificationwithansbandradar AT yierlin elderlycareactivitiesofdailylivingclassificationwithansbandradar AT qianhe elderlycareactivitiesofdailylivingclassificationwithansbandradar AT jordanelorandel elderlycareactivitiesofdailylivingclassificationwithansbandradar AT olivierromain elderlycareactivitiesofdailylivingclassificationwithansbandradar |
_version_ |
1721567573715189760 |