Rural Folk Dance Movement Recognition Based on an Improved MCM-SVM Model in Wireless Sensing Environment

Abstract

In order to improve the accuracy and timeliness of folk dance movement recognition, this paper proposes an improved MCM-SVM recognition model to recognize the lower limb human motion of ethnic dance in rural areas based on sensors. In order to recognize these actions, the SVM algorithm is used to identify the current action, and the MCM is used to optimize the recognition result. The experimental results show that the proposed improved model achieves higher recognition rate compared to the SVM algorithm for the recognition of different dance moves. The average recognition rate exceeds 93%, and the average recognition time is about 0.6 ms, which verifies the effectiveness of the proposed model. The proposed model will provide guidance and practicality for the design and construction of future dance movement recognition systems.