Application of Real-time Motion Capture Technology in Street Dance Movement Analysis and Optimization

With the advancement of motion capture technology, its application in dance movement training has become increasingly prevalent. This study explores the use of real-time dynamic capture technology for the analysis and optimization of street dance movements. A street dance movement system based on real-time motion capture technology is designed. Initially, the data obtained from sensors are fused using the Extended Kalman Filtering gesture fusion algorithm to identify street dance movements. Subsequently, the gestures of the street dance movements are matched with template movements using the DTW algorithm, facilitating movement optimization. Finally, the system is scrutinized for its performance and application analysis. The number of concurrent users of the system is in the interval of [90,99], and the average response time for uploading street dance moves and street dance move evaluation is 7.631s and 0.35s, respectively, which basically meets the design objectives. The maximum error, average absolute error, and root mean square error of the pose angles solved by the algorithm in this paper do not exceed ±1.61°, 0.20°, and 0.25°, respectively, and the algorithm is highly accurate and smooth, which meets the requirements of pose solving. Through the evaluation of 12 training learning results of 6 testers, it is found that the movements of the testers are more and more similar to the template movements after several training sessions, which verifies that the system in this paper can guide the learners to learn and optimize the street dance movements.