{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T17:08:31Z","timestamp":1777223311633,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["18K18463"],"award-info":[{"award-number":["18K18463"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To develop a daily monitoring system for early detection of fall risk of elderly people during walking, this study presents a highly accurate micro-Doppler radar (MDR)-based gait classification method for the young and elderly adults. Our method utilizes a time-series of velocity corresponding to leg motion during walking extracted from the MDR spectrogram (time-velocity distribution) in an experimental study involving 300 participants. The extracted time-series was inputted to a long short-term memory recurrent neural network to classify the gaits of young and elderly participant groups. We achieved a classification accuracy of 94.9%, which is significantly higher than that of a previously presented velocity-parameter-based classification method.<\/jats:p>","DOI":"10.3390\/s21113643","type":"journal-article","created":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T23:35:05Z","timestamp":1621899305000},"page":"3643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network"],"prefix":"10.3390","volume":"21","author":[{"given":"Sora","family":"Hayashi","sequence":"first","affiliation":[{"name":"Graduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2088-1231","authenticated-orcid":false,"given":"Kenshi","family":"Saho","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan"},{"name":"Graduate School of Engineering, Toyama Prefectural University, Toyama 939-0398, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keitaro","family":"Shioiri","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Toyama Prefectural University, Toyama 939-0398, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Fujimoto","sequence":"additional","affiliation":[{"name":"Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Chiba 277-0882, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masao","family":"Masugi","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.archger.2012.05.010","article-title":"Identification of high risk fallers among older people living in residential care facilities: A simple screen based on easily collectable measures","volume":"55","author":"Whitney","year":"2012","journal-title":"Archi. 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