{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T13:21:36Z","timestamp":1783430496242,"version":"3.54.6"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With aging, cerebrovascular diseases can occur more often. Stroke cases involve hemiplegia, which causes difficulties in performing activities of daily living. Existing rehabilitation treatments are based on the subjective evaluation of the therapist as the need for non-contact care arises; it is necessary to develop a system that can self-rehabilitate and offer objective analysis. Therefore, we developed rehabilitation tools that enable self-rehabilitation exercises in a virtual space based on haptics. Thirty adults without neurological damage were trained five times in a virtual environment, and the time, number of collisions, and coordinates were digitized and stored in real time. An analysis of variance (ANOVA) of the time and distance similarity changes revealed that as the number of rounds increased, no changes or increases occurred (p \u2265 0.05), and the collisions and paths were stable as the training progressed (p &lt; 0.05). ANOVA showed a high correlation (0.90) with a decrease in the number of crashes and time required. It was meaningful to users when performing rehabilitation training more than four times and significantly impacted the analysis. This study analyzed the upper limb and cognitive rehabilitation of able-boded people in three-dimensional space in a virtual environment; the performance difficulty could be controlled through variations in rehabilitation models.<\/jats:p>","DOI":"10.3390\/s21082790","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"2790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Upper Limb Rehabilitation Tools in Virtual Reality Based on Haptic and 3D Spatial Recognition Analysis: A Pilot Study"],"prefix":"10.3390","volume":"21","author":[{"given":"Eun Bin","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Software Convergence, Graduate School, Soonchunhyang University, 22, Soonchunhyang-ro, Asan City 31538, Chungnam-do, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Medical IT Engineering, College of Medical Sciences, Soonchunhyang University, 22, Soonchunhyang-ro, Asan City 31538, Chungnam-do, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3696-9353","authenticated-orcid":false,"given":"Onseok","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Graduate School, Soonchunhyang University, 22, Soonchunhyang-ro, Asan City 31538, Chungnam-do, Korea"},{"name":"Department of Medical IT Engineering, College of Medical Sciences, Soonchunhyang University, 22, Soonchunhyang-ro, Asan City 31538, Chungnam-do, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.humov.2005.10.005","article-title":"Interlimb coordination following stroke","volume":"24","author":"Garry","year":"2005","journal-title":"Hum. 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