{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:01:14Z","timestamp":1764403274865,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University Creative Asset Commercialization Support (BRIDEGE+)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson\u2019s disease. We previously designed a rehabilitation assist device that can detect and classify a user\u2019s gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 \u00b1 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation \u22120.9996, p &lt; 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p &lt; 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications.<\/jats:p>","DOI":"10.3390\/s23146638","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T01:32:10Z","timestamp":1690248730000},"page":"6638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1011-6985","authenticated-orcid":false,"given":"Hyeonjong","family":"Kim","sequence":"first","affiliation":[{"name":"Division of Mechanical Engineering, (National) Korea Maritime and Ocean University, Busan 49112, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji-Won","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Biomedical Engineering, Konkuk University, Chungju 27478, Republic of Korea"},{"name":"BK21 Plus Research Institute of Biomedical Engineering, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6871-2635","authenticated-orcid":false,"given":"Junghyuk","family":"Ko","sequence":"additional","affiliation":[{"name":"Division of Mechanical Engineering, (National) Korea Maritime and Ocean University, Busan 49112, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1177\/0891988718819858","article-title":"Music Therapy and Dance as Gait Rehabilitation in Patients with Parkinson Disease: A Review of Evidence","volume":"32","author":"Pereira","year":"2019","journal-title":"J. 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