The analysis of motion recognition model for badminton player movements using machine learning.

This study aims to comprehensively analyze and classify the badminton players' swing actions by combining the theoretical frameworks of quantum mechanics and machine learning. A badminton stroke recognition method based on Quantum Convolutional Neural Network (QCNN) is proposed. It is then compared with traditional Support Vector Machines (SVM) and Convolutional Neural Network (CNN). The comparison aims to assess the classification performance and robustness of each method. Firstly, this study collects the badminton players' stroke action data using high-frame-rate cameras and inertial sensors to record posture information during different strokes. OpenPose is used for human posture estimation, and combined with sensor data, spatiotemporal features during the stroke are extracted. Next, during the data preprocessing stage, Gaussian filtering is applied to remove noise, followed by normalization and feature selection to ensure the quality of the model input data. Then, SVM, CNN, and QCNN models are trained to classify different stroke actions. To evaluate model performance, precision, recall, and F1-score are selected as metrics. Experiments with varying noise levels (low, medium, and high noise) are designed to test the models' robustness. Finally, decision tree feature importance analysis is conducted to assess the contribution of different features to stroke action classification. Experimental results show that QCNN outperforms all other models in all classification tasks, with an F1-score of 0.860 for backhand intercept, significantly better than CNN (0.792) and SVM (0.753). In robustness tests under low, medium, and high noise environments, the classification precision of QCNN is 0.95, 0.92, and 0.89, respectively. This clearly surpasses both CNN and SVM. The results indicate that QCNN has strong adaptability to noisy data. Further feature analysis reveals that arm angle, twist angle, and step position are key factors affecting classification accuracy, with the highest contribution in the QCNN model. This study validates the superiority of QCNN in badminton action recognition and provides reliable methodological support for subsequent sports technique analysis.