Abstract
The integration of sports science and camera sensing technology has recently emerged to be an advanced analytical tool in sportsperson performance enhancement. The use of computing power and a web-based application can provide quick information analysis and data reporting between coaches and athletes. The design of an archery analytic workflow is demonstrated in this paper using the Python Flask framework, video analytic algorithms, a structured video inventory framework, MongoDB database setup and integration of the Keypoint R-CNN machine learning backend. A user-friendly data visualisation interface on the front end is integrated in the software to deliver athletes’ analytical capabilities such as thorough frame-by-frame video analysis, posture consistency estimation and joint kinematic analysis. This web application framework is not limited to archery sports, and can be extended to numerous sports, such as shooting, weightlifting and cycling. The significance of integrating camera sensing technology with the sports science field can offer quantitative and qualitative observations to improve training programs and performance evaluation.
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