{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:07:09Z","timestamp":1764785229860,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684505"},{"type":"electronic","value":"9781643684512"}],"license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,20]]},"abstract":"<jats:p>Hand and joint mobility recovery involve performing a set of exercises. Gestures are often used in the hand mobility recovery process. This paper discusses the selection and the use of 2 models of neural networks for the classification of data that describe Leap Motion gestures. The gestures are: the hand opening and closing gesture and the palm rotation gesture. The purpose is the optimal selection of the neural network model to be used in the classification of the data describing the recovery gestures. The models chosen for the classification of the two gestures were: Linear Discriminant Analysis (LDA) and K-neighbors Classifier (KNN). The accuracies achieved in the classification of the gestures for each model are: 0.91 \u2013 LDA and 0.98 \u2013 KNN.<\/jats:p>","DOI":"10.3233\/shti230739","type":"book-chapter","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:16:06Z","timestamp":1698048966000},"source":"Crossref","is-referenced-by-count":2,"title":["Recovery Gestures Classification Using KNN and LDA Models"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4878-6746","authenticated-orcid":false,"given":"Stelian","family":"Nicola","sequence":"first","affiliation":[{"name":"Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6165-1710","authenticated-orcid":false,"given":"Oana-Sorina","family":"Chirila","sequence":"additional","affiliation":[{"name":"Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9700-7732","authenticated-orcid":false,"given":"Lacramioara","family":"Stoicu-Tivadar","sequence":"additional","affiliation":[{"name":"Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Telehealth Ecosystems in Practice"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230739","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:16:09Z","timestamp":1698048969000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,20]]},"ISBN":["9781643684505","9781643684512"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230739","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2023,10,20]]}}}