Deep learning for sports motion recognition with a high-precision framework for performance enhancement

Sports motion recognition is essential for performance analysis, injury prevention, and athlete monitoring. Traditional deep learning models, such as Long Short-Term Memory (LSTM) and Transformer-based architectures, struggle to capture motion dynamics with long-term dependencies or noise in their inputs. To overcome these limitations, this work proposes an Evolved Parallel Recurrent Network (EPRN) with wavelet transforms for high-precision motion recognition. The EPRN framework utilizes parallel recurrent pathways to enhance temporal modeling, while wavelet-based feature extraction preserves the fine-grained details of motion at multiple spatial and temporal resolutions. The proposed method has been tested on benchmark sports motion data and compared with several common architectures, including LSTM, Gated Recurrent Units (GRUs), and Convolutional Neural Network (CNN) models. The experiments demonstrated that EPRN outperforms the other models, reducing the root mean squared error (RMSE) by 23.5% and increasing the structural similarity index (SSIM) by 12.7%, indicating its effectiveness in reconstructing motion trajectories with reduced error. Furthermore, the residual analysis confirms the result that EPRN has lower error variability and less sensitivity to abrupt motion transitions, thus being a more robust solution for real-world applications. The results, therefore, indicate that combining wavelet-transform-based feature extraction with recurrent deep learning significantly enhances the accuracy of motion recognition. The real-life applications of this work include sports performance analysis, real-time motion tracking, and rehabilitation systems. Future work will focus on multimodal data fusion (e.g., video plus wearable sensor data) and also lightweight EPRN variants suitable for real-time applications.