{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T11:54:14Z","timestamp":1791806054462,"version":"4.3.4"},"reference-count":323,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T00:00:00Z","timestamp":1641168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) \u201cSmart gait\u201d applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.<\/jats:p>","DOI":"10.3389\/frobt.2021.749274","type":"journal-article","created":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T07:34:47Z","timestamp":1641195287000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":128,"title":["A Survey of Human Gait-Based Artificial Intelligence Applications"],"prefix":"10.3389","volume":"8","author":[{"given":"Elsa J.","family":"Harris","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I-Hung","family":"Khoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emel","family":"Demircan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,1,3]]},"reference":[{"key":"B1","first-page":"5640","article-title":"Multi-domain Data Capture and Cloud Buffered Multimodal Evaluation Platform for Clinical Assessment of Cerebellar Ataxia","author":"Abeysekara","year":"2020"},{"key":"B2","doi-asserted-by":"publisher","first-page":"7472039","DOI":"10.1155\/2019\/7472039","article-title":"Knee Joint Biomechanical Gait Data Classification for Knee Pathology Assessment: A Literature Review","volume":"2019","author":"Abid","year":"2019","journal-title":"Appl. bionics Biomech."},{"key":"B3","doi-asserted-by":"publisher","first-page":"158820","DOI":"10.1109\/access.2019.2945545","article-title":"Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition","volume":"7","author":"Abiodun","year":"2019","journal-title":"IEEE Access"},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.1145\/3347450.3357663","article-title":"MultiLock: Mobile Active Authentication Based on Multiple Biometric and Behavioral Patterns","author":"Acien","year":"2019"},{"key":"B5","doi-asserted-by":"publisher","first-page":"30942","DOI":"10.1109\/access.2018.2840327","article-title":"Meta-Classifiers in Huntington's Disease Patients Classification, Using iPhone's Movement Sensors Placed at the Ankles","volume":"6","author":"Acosta-Escalante","year":"2018","journal-title":"IEEE Access"},{"key":"B6","doi-asserted-by":"publisher","first-page":"100352","DOI":"10.1109\/access.2021.3095477","article-title":"Learning Age from Gait: A Survey","volume":"9","author":"Aderinola","year":"2021","journal-title":"IEEE Access"},{"key":"B7","first-page":"355","article-title":"A Machine Learning Approach to Discriminate the Parkinsons Disease from Other Neurological Diseases Using Different Feature Selection Techniques Based on Gait Parameters","author":"Aich","year":"2018"},{"key":"B8","first-page":"108","article-title":"A Pervasive and Sensor-free Deep Learning System for Parkinsonian Gait Analysis","author":"Ajay","year":"2018"},{"key":"B9","first-page":"1","article-title":"Improved Gait Recognition Based on Specialized Deep Convolutional Neural Networks","author":"Alotaibi","year":"2015"},{"key":"B10","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/tfuzz.2011.2171973","article-title":"Human Gait Modeling Using a Genetic Fuzzy Finite State Machine","volume":"20","author":"Alvarez-Alvarez","year":"2012","journal-title":"IEEE Trans. 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