Pattern recognition and classification of physical education teaching movements based on biomechanics
Abstract
In physical education teaching, motion analysis techniques are very crucial in teaching, standardizing the methods of teaching, and improving student performance. This study examines biomechanics and its application on how deep learning (DL) techniques together with motion capture data can be used for classifying and recognizing the different movements in teaching PE. Using stationary cameras, the collection includes high-quality motion capture recordings of various PE activities, such as running, passing, jumping, crossing, and dribbling. Normalization and noise reduction are preprocessing processes that help to improve the quality and integrity of the data. Biomechanical characteristics are gathered to depict movement dynamics that incorporate significant elements, such as the angle of joints, angular velocity, angular acceleration, and joint displacement. The Intelligent Tunicate Swarm Search deep convolutional recurrent neural network (ITS-DCRNN) technique uses these characteristics as inputs for classification models. The proposed model is assessed on various types of metrics, including accuracy (98.58%), precision (98.23%), recall (98.87%), and F1-score (98.89%). The results show that the suggested system of teaching assessments is effective. According to the findings, biomechanics-based pattern recognition can improve PE teaching methods by providing educators with data-driven insights on movement performance and areas for improvement. This strategy can result in more efficient teaching techniques, improving student learning results while lowering the chance of harm.
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