{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T05:22:27Z","timestamp":1776489747651,"version":"3.51.2"},"reference-count":57,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Slovenian Research Agency","award":["P2-0123"],"award-info":[{"award-number":["P2-0123"]}]},{"name":"Slovenian Research Agency","award":["1000-2022-0552"],"award-info":[{"award-number":["1000-2022-0552"]}]},{"DOI":"10.13039\/501100004329","name":"Slovenian Research Agency","doi-asserted-by":"publisher","award":["P2-0123"],"award-info":[{"award-number":["P2-0123"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004329","name":"Slovenian Research Agency","doi-asserted-by":"publisher","award":["1000-2022-0552"],"award-info":[{"award-number":["1000-2022-0552"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human gait activity recognition is an emerging field of motion analysis that can be applied in various application domains. One of the most attractive applications includes monitoring of gait disorder patients, tracking their disease progression and the modification\/evaluation of drugs. This paper proposes a robust, wearable gait motion data acquisition system that allows either the classification of recorded gait data into desirable activities or the identification of common risk factors, thus enhancing the subject\u2019s quality of life. Gait motion information was acquired using accelerometers and gyroscopes mounted on the lower limbs, where the sensors were exposed to inertial forces during gait. Additionally, leg muscle activity was measured using strain gauge sensors. As a matter of fact, we wanted to identify different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of attention-based convolutional and recurrent neural networks algorithms outperformed the other tested algorithms and was individually tested further on the datasets of five subjects and delivered the following averaged results of classification: 98.9% accuracy, 96.8% precision, 97.8% sensitivity, 99.1% specificity and 97.3% F1-score. Moreover, the algorithm\u2019s robustness was also verified with the successful detection of freezing gait episodes in a Parkinson\u2019s disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.<\/jats:p>","DOI":"10.3390\/s23020745","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T07:05:09Z","timestamp":1673247909000},"page":"745","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Human Gait Activity Recognition Machine Learning Methods"],"prefix":"10.3390","volume":"23","author":[{"given":"Jan","family":"Slemen\u0161ek","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia"}]},{"given":"Iztok","family":"Fister","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8693-3247","authenticated-orcid":false,"given":"Jelka","family":"Ger\u0161ak","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia"}]},{"given":"Bo\u017eidar","family":"Bratina","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4653-8503","authenticated-orcid":false,"given":"Vesna Marija","family":"van Midden","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Clinical Centre, 1000 Ljubljana, Slovenia"}]},{"given":"Zvezdan","family":"Pirto\u0161ek","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Clinical Centre, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6856-7992","authenticated-orcid":false,"given":"Riko","family":"\u0160afari\u010d","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"543","DOI":"10.2106\/00004623-195335030-00003","article-title":"The Major Determinants in Normal and Pathological Gait","volume":"35","author":"Saunders","year":"1953","journal-title":"J. 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