{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T03:54:57Z","timestamp":1778039697267,"version":"3.51.4"},"reference-count":18,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Oro Muscles B.V. 9715 CJ Groningen"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to &lt;5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population.<\/jats:p>","DOI":"10.3390\/s22134957","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T01:40:36Z","timestamp":1656639636000},"page":"4957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools"],"prefix":"10.3390","volume":"22","author":[{"given":"Christian","family":"Greve","sequence":"first","affiliation":[{"name":"Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands"},{"name":"Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hobey","family":"Tam","sequence":"additional","affiliation":[{"name":"Oro Muscles B.V., 9715 CJ Groningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8792-6508","authenticated-orcid":false,"given":"Manfred","family":"Grabherr","sequence":"additional","affiliation":[{"name":"Oro Muscles B.V., 9715 CJ Groningen, The Netherlands"},{"name":"Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6270-3295","authenticated-orcid":false,"given":"Aditya","family":"Ramesh","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bart","family":"Scheerder","sequence":"additional","affiliation":[{"name":"Center for Development and Innovation (CDI), University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands"},{"name":"Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juha M.","family":"Hijmans","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1097\/MRR.0b013e32833f0500","article-title":"The recovery of walking in stroke patients: A review","volume":"33","author":"Jang","year":"2010","journal-title":"Int. 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