Evaluation of Human Action Based on Feature-Weighted Dynamic Time Warping

Action evaluation can automatically detect abnormal actions by evaluating the quality of human actions in specific postures, which is widely used in the field of rehabilitation medicine. This paper proposes an intelligent rehabilitation action evaluation system to evaluate the quality of patients’ actions during rehabilitation training, which helps medical professionals to more effectively monitor and guide the process, thus improving rehabilitation effects. Firstly, we collected human skeletal key-point data based on a depth camera and processed these data with gap filling and filtering; then, the effective data segments were segmented from the whole action dataset, angle and distance features were extracted, and the feature matrix was obtained; then, we used the Euclidean Barycenter Dynamic Time Warping–Barycenter Averaging algorithm to produce action templates; finally, we proposed a Feature-Weighted Dynamic Time Warping algorithm to calculate the similarity between the detected action and the template action and established an action achievement score mechanism to evaluate the rehabilitation action. The experimental results show that compared with the action evaluation method based on feature-matrix DTW, the proposed method significantly improves the similarity between healthy people and patients, and the similarity improvement for patients is more significant. Based on similarity scores, the difference between the actions of healthy people and patients and the template actions is more than 80%, which shows that the method can evaluate action quality in people with different health conditions and effectively reduce error in action evaluation. The confidence level of the action achievement score mechanism reaches 99%, which meets the actual application requirements.