A Survey of Human Gait-Based Artificial Intelligence Applications
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 an...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2021.749274/full |
| _version_ | 1818733002558013440 |
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| author | Elsa J. Harris I-Hung Khoo I-Hung Khoo Emel Demircan Emel Demircan |
| author_facet | Elsa J. Harris I-Hung Khoo I-Hung Khoo Emel Demircan Emel Demircan |
| author_sort | Elsa J. Harris |
| collection | DOAJ |
| description | 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) “Smart gait” 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. |
| first_indexed | 2024-12-17T23:42:32Z |
| format | Article |
| id | doaj.art-a7d79609b79f4992a04bb6c646d0103c |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-17T23:42:32Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-a7d79609b79f4992a04bb6c646d0103c2022-12-21T21:28:24ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442022-01-01810.3389/frobt.2021.749274749274A Survey of Human Gait-Based Artificial Intelligence ApplicationsElsa J. Harris0I-Hung Khoo1I-Hung Khoo2Emel Demircan3Emel Demircan4Human Performance and Robotics Laboratory, Department of Mechanical and Aerospace Engineering, California State University Long Beach, Long Beach, CA, United StatesDepartment of Electrical Engineering, California State University Long Beach, Long Beach, CA, United StatesDepartment of Biomedical Engineering, California State University Long Beach, Long Beach, CA, United StatesHuman Performance and Robotics Laboratory, Department of Mechanical and Aerospace Engineering, California State University Long Beach, Long Beach, CA, United StatesDepartment of Biomedical Engineering, California State University Long Beach, Long Beach, CA, United StatesWe 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) “Smart gait” 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.https://www.frontiersin.org/articles/10.3389/frobt.2021.749274/fullreviewhuman gait analysisbiometricsmachine learningartificial intelligence |
| spellingShingle | Elsa J. Harris I-Hung Khoo I-Hung Khoo Emel Demircan Emel Demircan A Survey of Human Gait-Based Artificial Intelligence Applications Frontiers in Robotics and AI review human gait analysis biometrics machine learning artificial intelligence |
| title | A Survey of Human Gait-Based Artificial Intelligence Applications |
| title_full | A Survey of Human Gait-Based Artificial Intelligence Applications |
| title_fullStr | A Survey of Human Gait-Based Artificial Intelligence Applications |
| title_full_unstemmed | A Survey of Human Gait-Based Artificial Intelligence Applications |
| title_short | A Survey of Human Gait-Based Artificial Intelligence Applications |
| title_sort | survey of human gait based artificial intelligence applications |
| topic | review human gait analysis biometrics machine learning artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2021.749274/full |
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