Sanda posture recognition using feature extraction algorithms

Computer vision and machine learning techniques play a pivotal role in advancing the recognition and analysis of dynamic human postures, particularly in sports such as Sanda, a form of Chinese martial arts. This paper explores the application of feature extraction algorithms for the efficient recognition of Sanda postures. By utilizing sensor data, including motion trajectories, joint angles, and body part coordinates, we develop a robust algorithmic framework to identify and classify various stances and movements in real-time. The research integrates traditional image processing methods with modern machine learning approaches, such as deep learning and support vector machines (SVM), to enhance the accuracy and efficiency of posture recognition. Experimental evaluations using video sequences and motion-capture data show that deep learning models, particularly convolutional neural networks (CNNs), outperform traditional techniques in both speed and precision. The findings highlight the potential of feature extraction algorithms in the development of intelligent systems for automating posture recognition in martial arts, offering significant improvements in training, performance monitoring, and real-time feedback. This work paves the way for data-driven sports methodologies, improving the effectiveness of martial arts training through advanced computational techniques.