How to improve your running efficiency with Computer Vision?
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TLDR: I have created an experiment — iterating through various shoe types to achieve Eliud Kipchoge Running Efficiency. By surprise — it turns out that Flip-flops suck for running and Carbon-fiber shoes are epic!
Abstract
Running efficiency — the ability to expend minimal energy while covering a defined distance — is a major factor in athletic performance.
Traditional methods for assessing running efficiency mostly rely on subjective evaluations or invasive physiological measures — thus, often limiting applicability and objectivity. This experiment introduces a modern approach to measuring running efficiency using AI. In other words, I am using computer vision (CV) technology to evaluate the running efficiency. Employing MoveNet from TensorFlow, I extract 17 keypoints from video footage of professional runners, including Eliud Kipchoge. Eliud is the GOAT in marathon running and especially known for his exceptional efficiency. By analyzing keypoints that are extracted from the video material, I have developed a similarity regression function that quantifies the similarity between a runner’s gait and Kipchoge’s exemplary form. This function provides objective feedback on both posture (keypoints position relative to each other) and movement (keypoints movement from frame to frame). Based on this approach, I have conducted a comparative analysis of my own running style against Eliud while iterating various shoe types to get closer to Eliud performance.