{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:34:00Z","timestamp":1761294840995,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This study aims to explore the potential of exergaming (which can be used along with prescriptive medication for children with spinal muscular atrophy) and examine its effects on monitoring and diagnosis. The present study focuses on comparing models trained on joint data for gesture detection, which has not been extensively explored in previous studies. The study investigates three approaches to detect gestures based on 3D Microsoft Azure Kinect joint data. We discuss simple decision rules based on angles and distances to label gestures. In addition, we explore supervised learning methods to increase the accuracy of gesture recognition in gamification. The compared models performed well on the recorded sample data, with the recurrent neural networks outperforming feedforward neural networks and decision trees on the captured motions. The findings suggest that gesture recognition based on joint data can be a valuable tool for monitoring and diagnosing children with spinal muscular atrophy. This study contributes to the growing body of research on the potential of virtual solutions in rehabilitation. The results also highlight the importance of using joint data for gesture recognition and provide insights into the most effective models for this task. The findings of this study can inform the development of more accurate and effective monitoring and diagnostic tools for children with spinal muscular atrophy.<\/jats:p>","DOI":"10.3390\/info16050421","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T06:31:27Z","timestamp":1747809087000},"page":"421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparing Classification Algorithms to Recognize Selected Gestures Based on Microsoft Azure Kinect Joint Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Marc","family":"Funken","sequence":"first","affiliation":[{"name":"School of Business, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5636-1660","authenticated-orcid":false,"given":"Thomas","family":"Hanne","sequence":"additional","affiliation":[{"name":"Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/ITNEC52019.2021.9587199","article-title":"Design of joint range of motion measurement based on Kinect","volume":"Volume 5","author":"Gao","year":"2021","journal-title":"Proceedings of the 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gu, J., Yang, X., De Mello, S., and Kautz, J. 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