Improving Accuracy and Runtime of Skeletal Tracking of Lower Limbs for Athletic Jump Mechanics Assessment

Previous studies have shown that athletic jump mechanics assessments are valuable tools for identifying indicators of an individual's anterior cruciate ligament injury risk. These assessments, such as the drop jump test, often relied on camera systems or sensors that are not always accessible nor practical for screening individuals in a sports setting. As human pose estimation deep learning models improve, we envision transitioning biometrical assessments to mobile devices. As such, here we have addressed two of the most preclusive hindrances of the current state-of-the-art models: accuracy of the lower limb joint prediction and the slow run-time of in-the-wild inference. We tackle the issue of accuracy by adding a post-processing step that is compatible with all inference methods that outputs 3D key points. Additionally, to overcome the lengthy inference rate, we propose a depth estimation method that runs in real-time and can function with any 2D human pose estimation model that outputs COCO key points. Our solution, paired with a state-of-the-art model for 3D human pose estimation, significantly increased lower-limb positional accuracy. Furthermore, when paired with our real-time joint depth estimation algorithm, it is a plausible solution for developing the first mobile device prototype for athlete jump mechanics assessments.