Daniel C. Cao1 and Yu Sun2, 1USA, 2California State Polytechnic University, USA
This program addresses the shortfall in advanced technology application in swim training by introducing an innovative method that combines swimmer videos with AI for optimizing swimming starts. Utilizing common devices like smartphones for recording, alongside OpenCV for video processing and MediaPipe for accurate landmark detection, it navigates challenges in video input and output efficiently. The effectiveness of this approach was validated through two experiments focusing on the technology's accuracy and its impact on training quality. Results highlighted the technology's positive contribution to swim training, emphasizing its potential to revolutionize training techniques. This non-invasive, accessible solution emphasizes critical race aspects and offers scope for continuous innovation and improvement, demonstrating significant promise for enhancing athletic training across all levels.
Swim Training Technology, AI Landmark Detection, Pose Estimation, Innovation in Swim Training
