Applying Deep Learning to Image and IMU Sensor Data for Real-Time Fatigue Recognition in Table Tennis Players

Fatigue during table tennis training can impair performance and increase the risk of injury. This study proposes a real-time fatigue recognition system that integrates an embedded edge-AI racket with a camera for synchronized multimodal data acquisition. On the racket side, a neural network was deployed on an edge-AI microcontroller unit (MCU) to enable on-device inference, while the test data were transmitted to a computer for multimodal feature fusion. Given the continuous nature of fatigue development, the data were divided into three stages: non-fatigue, transition, and fatigue to facilitate progressive evaluation. In the binary classification task, which distinguished between the two extreme states, the multimodal hybrid model achieved an accuracy of 99%. When the transition stage was incorporated into a three-class classification task, performance declined because of the ambiguous boundaries between fatigue states. However, the use of multimodal decision-fusion strategies, such as weighted soft voting, maintained an accuracy of 96%. Overall, the results demonstrate the feasibility of deep learning for real-time fatigue recognition during table tennis training and provide a practical monitoring tool to help players track their physiological states and reduce injury risk.