Junxi Pan¹ and Garret Washburn², ¹USA, ²California Baptist University, USA

Rowing technique determines both performance and injury risk, yet most athletes outside elite programs lack access to the marker-based motion capture or full-time coaching that would let them quantify their form. This work presents StrokeSense, a mobile application that analyses rowing technique from ordinary smartphone video. A Flutter front-end uploads a clip to a Flask backend, where MediaPipe Pose extracts 33 body landmarks per frame, a biomechanical engine computes layback, leg, shin, elbow, and forward-layback angles, and a GPT-4 assistant turns the per-frame angle history into a structured coaching report delivered through Firebase. Experiments show landmark detection accuracy above 88% in every tested condition and a mean absolute joint angle error of 2.8°, well below the 5° threshold that coaches use informally. By turning a single phone recording into quantitative, narrated feedback, StrokeSense makes rigorous rowing analysis broadly accessible.

Rowing Biomechanics, Mediapipe, OpenCV, Flutter, Firebase