Research on a Motion-Sensing Dance Interaction System Based on Motion Evaluation Algorithms

With the advancement of human-computer interaction technology, motion-sensing interaction has gradually become a crucial technical tool in dance education and entertainment. To enhance the immersion and interactivity of motion-sensing dance systems, this study designs and implements a motion-sensing dance interaction system based on a motion evaluation algorithm. The paper first analyzes the current state of research on motion-sensing technology and motion evaluation algorithms, identifying existing challenges. Next, it proposes a motion evaluation algorithm that integrates deep learning with posture features, improving its accuracy and real-time performance through feature optimization and model training. Then, a complete system architecture for the motion-sensing dance interaction is designed, encompassing data collection, motion recognition, motion evaluation, and real-time interaction feedback modules. Finally, the system's effectiveness is validated through experiments. The results demonstrate that the system not only accurately evaluates users' dance movements but also offers excellent real-time responsiveness and user interaction experience. These findings provide technical support and reference for the further application of motion-sensing interaction technology in dance education and digital entertainment.