We develope a novel and user-friendly system that analyzes tennis shots using a sensor placed on the passive arm. Collecting Inertial Measurement Unit sensor data from 20 recreational tennis players, we implemented neural networks that exclusively utilize passive arm data to detect and classify six shots, achieving a classifcation accuracy of 88.2% and a detection F1 score of 86.0%, comparable to the dominant arm. These models were then incorporated into an end-to-end prototype, which records passive arm motion through a smartwatch and displays a summary of shots on a mobile app.
Related publications
1. J Park, S Yang, S Jo, Silent Impact: Tracking Tennis Shots from the Passive Arm, The ACM Symposium on User Interface Software and Technology 2024 (UIST 2024) [LINK] [PDF] [VIDEO]
