Research on Dance Movement Recognition and Assessment through Human Pose Estimation

摘要

As an artful movement, dance demands performers to execute precise actions and maintain graceful postures. However, evaluating a dancer's movements can be highly subjective and cumbersome. To address this issue, our study proposed an intelligent method for dance movement recognition and assessment based on human posture estimation. Regarding movement recognition, we refined the original COCO skeleton model, followed by a lightweight improvement to the OpenPose model. Compared to the original model, the parameter quantity decreased by 88.9%. We employed the improved OpenPose algorithm to extract key points of the human body from videos and used Singular Spectrum Analysis (SSA) to denoise the data. For the assessment aspect, we utilized the Dynamic Time Warping (DTW) algorithm to measure the similarity between actual dance movements and standard dance movements in terms of joint angles, thereby assessing the dance movements. This method can provide dancers with real-time feedback and guidance, objectively evaluating their dance movements. The proposed intelligent method can be used in controlling humanoid robotics or rehabilitation robotics due to the accurate human pose estimation and movement assessment results.