Gait Analysis Using Video for Disabled People in Marginalized Communities

Vairis, Achilles; Boyak, Johnathan; Brown, Suzana; Bess, Maurice; Bae, Kyu Hyun; Petousis, Markos

doi:10.1007/978-3-030-68452-5_14

Cited by 2 publications

(1 citation statement)

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“…However, it is essential to verify the effectiveness of automated gait analysis based on pose estimation in various clinical populations, including both adults and children. Previous research has indicated that current pose estimation algorithms can accurately track the gait of patient populations using walking aids [ 38 ], but tracking patient populations who use prosthetic devices that differ from those used to train the algorithms presents challenges [ 39 ]. Therefore, it is critical to validate the accuracy and reliability of automated gait analysis in diverse patient populations before implementing it in clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

Hii

Beng

Zainal

et al. 2023

Sensors

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Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.

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Section: Discussionmentioning

confidence: 99%