[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290427-105":3,"doc-detail-290427-en":81,"detail-sidebar-cat-0-en-105":98},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","human-gait-activity-recognition-machine-learning-methods-article","Human Gait Activity Recognition Machine Learning Methods - Article","","Human gait activity recognition advances motion analysis for practical deployments, especially monitoring gait disorder patients to support tracking disease progression and assessing drug effects. The study presents a robust wearable acquisition setup that records lower-limb inertial data from accelerometers and gyroscopes under gait forces, complemented by strain gauge muscle measurements. Machine learning models are trained and compared to classify activities and detect risk factors.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/human-gait-activity-recognition-machine-learning-methods-article/290427/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/human-gait-activity-recognition-machine-learning-methods-article/290427.png","ImageObject",300,407,{"name":42,"@type":43},"Ivy","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",15,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the study address in human gait activity recognition?","Question",{"text":63,"@type":64},"It targets reliable recognition of gait activities and risk factors from wearable motion data, with a focus on improving patient monitoring.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is gait data collected in the proposed system?",{"text":68,"@type":64},"Lower-limb accelerometers and gyroscopes capture inertial signals during walking, and strain gauge sensors measure leg muscle activity.",{"name":70,"@type":61,"acceptedAnswer":71},"Which machine learning approach performs best in the results?",{"text":72,"@type":64},"An attention-based combination of convolutional and recurrent neural networks achieves the top averaged classification performance, with strong robustness validated on freezing gait detection.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290427,1789641670,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":80,"read_time":97,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"sensors   \nArticle  \nHuman Gait Activity Recognition Machine Learning Methods  \nJan Slemenšek 1,*, Iztok Fister 2, Jelka Geršak 1, Božidar Bratina 2, Vesna Marija van Midden 3, Zvezdan Pirtošek 3 and Riko Šafariˇc 2  \nCitation: Slemenšek, J.; Fister, I.; Geršak, J.; Bratina, B.; van Midden, V.M.; Pirtošek, Z.; Šafariˇc, R. Human Gait Activity Recognition Machine Learning Methods. Sensors 2023, 23, 745. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s23020745  \nAcademic Editor: Antonio Fern¡ndez-Caballero  \nReceived: 9 December 2022  \nRevised: 23 December 2022  \nAccepted: 3 January 2023  \nPublished: 9 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia  \n2 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia  \n3 Department of Neurology, University Clinical Centre, 1000 Ljubljana, Slovenia  \n* [Correspondence: jan.slemensek@gmail.com](Correspondence: jan.slemensek@gmail.com)  \nAbstract: 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's 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's robustness was also verified with the successful detection of freezing gait episodes in a Parkinson's disease patient. The results of this study indicate a feasible gait event classification method capable of complete algorithm personalization.  \nKeywords: human gait; activity recognition; wearable; machine learning; convolutional neural network; recurrent neural network; attention mechanism  \n1. Introduction  \nHuman locomotion is an extraordinarily complex translational motion that can be described on the basis of the kinematics and muscular activity of the extremities in all their various movements [1] . The analysis of human gait identifies and analyzes walking and posture problems, load anomalies and muscle failure, which would not be measurable with normal clinical exams. It can be defined as a pattern of the locomotion characteristic of a limited range of speeds and serves as an important diagnostic tool in many fields, such as health, sports, daily life activities, prosthetics, and can thus have a significant impact on the person's Quality of Life (QOL) [2] .  \nThe analysis of human gait cyclic motion is usually obtained from i","cbCaidEYuNEKaJZN","https://ap.wps.com/l/cbCaidEYuNEKaJZN","pdf",5362574,24,"English","# Introduction\n## Human gait and locomotion overview\n## Wearable sensing and machine learning setup\n## Feature extraction and classification approach","[{\"question\":\"What problem does the study address in human gait activity recognition?\",\"answer\":\"It targets reliable recognition of gait activities and risk factors from wearable motion data, with a focus on improving patient monitoring.\"},{\"question\":\"How is gait data collected in the proposed system?\",\"answer\":\"Lower-limb accelerometers and gyroscopes capture inertial signals during walking, and strain gauge sensors measure leg muscle activity.\"},{\"question\":\"Which machine learning approach performs best in the results?\",\"answer\":\"An attention-based combination of convolutional and recurrent neural networks achieves the top averaged classification performance, with strong robustness validated on freezing gait detection.\"}]","Human Gait Activity Recognition Machine Learning Methods - Article | PDF",60,{"code":4,"msg":82,"data":99},[100,104,108,112,116,121,126,129,134,137,141],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},"Exam",70,"exam",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":97,"slug":115},5,"Comic","comic",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":118,"show_sort_weight":119,"slug":120},6,"Technology",50,"technology",{"id":122,"doc_module":4,"doc_module_name":25,"category_name":123,"show_sort_weight":124,"slug":125},7,"Healthcare",40,"healthcare",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":127,"slug":128},30,"research-report",{"id":130,"doc_module":4,"doc_module_name":25,"category_name":131,"show_sort_weight":132,"slug":133},9,"Religion & Spirituality",20,"religion-spirituality",{"id":132,"doc_module":4,"doc_module_name":25,"category_name":135,"show_sort_weight":132,"slug":136},"World Cup","world-cup",{"id":138,"doc_module":4,"doc_module_name":25,"category_name":139,"show_sort_weight":138,"slug":140},10,"Lifestyle","lifestyle",{"id":142,"doc_module":4,"doc_module_name":25,"category_name":143,"show_sort_weight":113,"slug":144},19,"General","general"]