A proposed computer vision model for running gait assessment

Fraser Young, Rachel Mason, Jason Moore, Samuel Stuart, Rosie Morris, Alan Godfrey*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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).

Original languageEnglish
Title of host publication2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Place of PublicationPiscataway
Number of pages4
ISBN (Electronic)9781728127828
ISBN (Print)9781728127835
Publication statusPublished - 11 Jul 2022


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