Rehabilitation movement evaluation technique based on three-dimensional coordinate data

Accurate assessment of rehabilitation movements is crucial for improving the rehabilitation outcome of patients. However, traditional assessment methods have problems such as incomplete data and interfering information, and the study proposes a rehabilitation movement assessment technique based on 3D coordinate data to improve the accuracy and efficiency of the assessment. The study used Azure Kinect to acquire 3D coordinate data of the human body and utilized a dynamic time regularization algorithm to assess rehabilitation movements. The results show that the improved dynamic time regularization algorithm outperforms the traditional algorithm in terms of action segmentation accuracy, feature extraction accuracy, and action assessment accuracy. The average similarity of the improved dynamic temporal regularization algorithm reaches 0.9958, which is significantly higher than other algorithms. The technique proposed in the study can effectively improve the accuracy of rehabilitation action assessment, which has important application value in the field of rehabilitation medicine.