Swimming posture recognition using inertial sensors and CNN-SVM: Unveiling the cellular molecular biomechanics nexus

The biomechanical mechanisms of swimming involve a number of aspects. The forces exerted by muscles during different swimming postures are crucial. These muscle contractions and relaxations follow specific biomechanical principles. This work aims to develop a swimming posture recognition system based on inertial sensors and a Convolutional Neural Network-Support Vector Machine (CNN-SVM) to improve the accuracy and real-time performance of posture recognition. First, an inertial sensor system to be worn on swimwear is designed to collect three-axis motion data, including acceleration, angular velocity, and magnetometer readings. The collected data are then preprocessed through denoising, normalization, and feature extraction steps to ensure high-quality input data. Next, a Convolutional Neural Network (CNN) is constructed to automatically extract high-level features from the preprocessed sensor data. The CNN model, through multi-layer convolution and pooling operations, effectively captures the spatiotemporal patterns in the motion data, extracting highly distinguishable features for posture recognition. To further improve the model’s classification performance, a Support Vector Machine (SVM) classifier is applied based on the CNN model. Specifically, CNN is responsible for feature extraction, while the SVM handles the final posture classification. Cross-validation is used to train and validate the model, assessing its performance. Experimental results show that the model achieves a 95% accuracy rate on the training dataset and maintains an accuracy rate above 93% on the test dataset. The system can accurately and in real-time recognize various swimming postures, including freestyle, breaststroke, backstroke, and butterfly. The recognition accuracy for all four swimming styles exceeds 91%. Understanding these biomechanical mechanisms helps in improving the accuracy of the recognition system. In summary, the proposed method for swimming posture recognition based on inertial sensors and CNN-SVM has significant advantages in accuracy and real-time performance. It allows for better interpretation of the sensor data and more precise identification of different postures. The high accuracy and generalization ability of the proposed system suggest that it can effectively capture and analyze the biomechanical nuances of swimming, providing valuable insights for swimming training and performance evaluation, and opening up new avenues for intelligent sports monitoring. evaluation.