{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T01:42:28Z","timestamp":1793410948001,"version":"4.5.0"},"reference-count":60,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004704","name":"National Research Council of Thailand","doi-asserted-by":"crossref","award":["N42A660976"],"award-info":[{"award-number":["N42A660976"]}],"id":[{"id":"10.13039\/501100004704","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for both patients and therapists. Robotic systems have been developed to help; however, they currently cannot detect the continuous gait phase in real time, hindering their effectiveness. To address this limitation, researchers have attempted to develop gait phase detection in general using fuzzy logic algorithms and neural networks. However, there is a paucity of research on real-time and continuous gait phase detection. In light of this gap, we propose an unsupervised learning method for real-time and continuous gait phase detection. This method employs windows of real-time trajectories and a pre-trained model, utilizing trajectories from treadmill walking data, to detect the real-time and continuous gait phase of human on overground locomotion. The neural network model that we have developed exhibits an average time error of less than 11.51 ms across all walking conditions, indicating its suitability for real-time applications. Specifically, the average time error during overground walking at different speeds is 11.20 ms, which is comparatively lower than the average time error observed during treadmill walking, where it is 12.42 ms. By utilizing this method, we can predict the real-time phase using a pre-trained model from treadmill walking data collected with a full motion capture system, which can be performed in a laboratory setting, thereby eliminating the need for overground walking data, which can be more challenging to obtain due to the complexity of the setting.<\/jats:p>","DOI":"10.1371\/journal.pone.0312761","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T17:23:03Z","timestamp":1730481783000},"page":"e0312761","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Unsupervised learning for real-time and continuous gait phase detection"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8134-9885","authenticated-orcid":true,"given":"Dollaporn","family":"Anopas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yodchanan","family":"Wongsawat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0198-6935","authenticated-orcid":true,"given":"Jetsada","family":"Arnin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"pone.0312761.ref001","doi-asserted-by":"crossref","DOI":"10.1155\/2011\/759764","article-title":"Lower-limb robotic rehabilitation: literature review and challenges","volume":"2011","author":"I D\u00edaz","year":"2011","journal-title":"Journal of Robotics"},{"issue":"5","key":"pone.0312761.ref002","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s12541-017-0081-9","article-title":"Development of real-time gait phase detection system for a lower extremity exoskeleton robot","volume":"18","author":"DH Lim","year":"2017","journal-title":"International Journal of Precision Engineering and Manufacturing"},{"issue":"1","key":"pone.0312761.ref003","first-page":"48","article-title":"Modeling and control of a robotic exoskeleton for gait rehabilitation","volume":"25","author":"X Liu","year":"2017","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"pone.0312761.ref004","first-page":"357","article-title":"A review of robotic gait rehabilitation for spinal cord injury","volume":"11","author":"Y Chen","year":"2018","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"pone.0312761.ref005","first-page":"24","article-title":"A review of robotic gait rehabilitation for stroke","volume":"12","author":"SH Kim","year":"2019","journal-title":"IEEE Reviews in Biomedical Engineering"},{"issue":"23","key":"pone.0312761.ref006","doi-asserted-by":"crossref","first-page":"R986","DOI":"10.1016\/S0960-9822(01)00581-4","article-title":"Central pattern generators and the control of rhythmic movements","volume":"11","author":"E Marder","year":"2001","journal-title":"Current biology"},{"issue":"1","key":"pone.0312761.ref007","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/7333.918277","article-title":"Real-time gait event detection for paraplegic FES walking","volume":"9","author":"MM Skelly","year":"2001","journal-title":"IEEE Transactions on neural systems and rehabilitation engineering"},{"key":"pone.0312761.ref008","first-page":"1","article-title":"A Low-Cost Instrumented Shoe System for Gait Phase Detection based on Foot Plantar Pressure Data","author":"X Hu","year":"2023","journal-title":"IEEE Journal of Translational Engineering in Health and Medicine"},{"key":"pone.0312761.ref009","doi-asserted-by":"crossref","unstructured":"Zhao X, Liu R, Ma T, Li H, Song Q. 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