A proposed computer vision model for running gait assessment

Young, Fraser, Mason, Rachel, Moore, Jason, Stuart, Sam, Morris, Rosie and Godfrey, Alan (2022) A proposed computer vision model for running gait assessment. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, Piscataway, NJ, pp. 4773-4776. ISBN 9781728127835, 9781728127828

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Official URL: https://doi.org/10.1109/EMBC48229.2022.9871739


Running gait assessment is critical in performance optimization and injury prevention. Traditional approaches to running gait assessment are inhibited by unnatural running environments (e.g., indoor lab), varied assessor (i.e., subjective experience) and high costs with traditional reference standard equipment. Thus, development of valid, reproduceable and low-cost approaches are key. Use of wearables such as inertial measurement units have shown promise but despite their flexible use in any environment and reduced cost, they often retain complexities such as connectivity to mobile platforms and stringent attachment protocols. Here, we propose a non-wearable camera-based approach to running gait assessment, focusing on identification of initial contact events within a runner's stride. We investigated different artificial intelligence and object tracking approaches to determine the optimal methodology. A cohort of 40 healthy runners were video recorded (240FPS, multi-angle) during 2-minute running bouts on a treadmill. Validation of the proposed approach is obtained from comparison to manually labelled videos. The computing vision approach can accurately identify initial contact events (ICC(2,1) = 0.902).

Item Type: Book Section
Subjects: C600 Sports Science
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Health and Life Sciences > Sport, Exercise and Rehabilitation
Depositing User: Elena Carlaw
Date Deposited: 04 Nov 2022 12:37
Last Modified: 04 Nov 2022 12:45
URI: https://nrl.northumbria.ac.uk/id/eprint/50538

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