Paper #6

Optimized Lightweight CNN for Error Action Recognition in Physical Education Teaching

Shu Zhang and Jacklyn Anne D. Toldoya

Abstract: With the improvement of living standards and the enhancement of people's health awareness, participation in sports activities has received widespread attention. Traditional sports equipment, especially in educational environments, lacks the necessary technological advancements to provide precise guidance. The gap between this demand and available resources often leads to incorrect teaching methods, which may hurt students' sports training. To address these challenges, this paper proposes a motion action recognition system utilizing lightweight convolutional neural networks (CNN). This method effectively reduces the noise of sensor data, improves the accuracy and reliability of data, and lays a solid foundation for model training through one-dimensional median filtering and Z-score standardization. Optimize the CNN architecture by adjusting key parameters such as network structure, convolution kernel size, and convolution stride, which are fine-tuned based on training data to maximize the model's recognition ability. The research results provide valuable insights into the effectiveness of teaching techniques and targeted feedback for improving sports training. After sufficient training, the system performed excellently on test data, accurately identifying erroneous movements across various sports actions, particularly in critical areas such as stroke movements, with an accuracy rate of up to 97.82% and an RMSE as low as 1.71%. These results demonstrate the model's high precision and robustness. The system has shown great potential in addressing the current shortage of professional coaches by providing automatic, real-time feedback on motion accuracy.

Keywords: CNN; Error Action Recognition; Lightweight Deep Learning; Sports Teaching; Sports Training.