# Evaluation of Human Action Based on Feature-Weighted Dynamic Time Warping
## 7 Conclusions  
The intelligent rehabilitation action evaluation system (IRAES) framework is proposed in this article; it improves the accuracy of action similarity assessment by improving the distance calculation method in the DTW algorithm and provides a novel approach for the development of intelligent rehabilitation systems. Compared with traditional rehabilitation action evaluation methods, DTW-based evaluation using feature matrix, and machine learning-based evaluation, the IRAES makes the following three main contributions:  
(1) Firstly, this paper proposes an intelligent rehabilitation action evaluation system framework (IRAES) based on the Kinect sensor, addressing the inefficiency, cumbersomeness, and subjective bias of traditional assessment methods. The system uses Kinect to capture the three-dimensional coordinate data of key points of the human skeleton and displays the patient's movement assessment results in real time, which helps doctors adjust the rehabilitation plan in time. Compared with manual observation, this system provides a more objective, accurate, and real-time assessment of action quality. In particular, the DTW algorithm based on feature weights can adaptively adjust the alignment of action  
Figure 13: Score–similarity fitting results ((**a–d**) are the four actions of drinking water, combing hair, touching the opposite shoulder, and touching the back pocket, respectively). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multicolumn{2}{c}{**Drinking Water**} & \multicolumn{2}{c}{**Combing Hair**} & \multicolumn{2}{c}{**Touching the Opposite Shoulder**} & \multicolumn{2}{c}{**Touching the Back Pocket**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Similarity Score**} & \multicolumn{2}{c}{**Similarity Score**} & \multicolumn{2}{c}{**Similarity Score**} & \multicolumn{2}{c}{**Similarity Score**} \\ \hline
1 & 0.9999 & 4 & 0.9998 & 4 & 0.9995 & 4 & 0.9995 & 4 \\
2 & 0.9997 & 4 & 0.9998 & 4 & 0.9992 & 4 & 0.9992 & 4 \\
3 & 0.9997  
sequences, effectively balance the weights of angle and distance features, reduce errors, and improve the accuracy and robustness of action evaluation. (2) Secondly, in view of the limitations of existing studies that primarily focus on data from healthy individuals, this paper constructed a comprehensive dataset containing action data of healthy people and patients for the IRAES. Compared with studies that only involve simple actions such as standing, sitting, and walking or only focus on the data of healthy people, this paper better aligns with the clinical application requirements in terms of action types and population coverage. By analyzing the similarity differences in four actions between healthy individuals and patients, limb disability can be tested, providing references for personalized rehabilitation plans. In addition, this paper proposes a DBA-based action template creation algorithm, which generates action templates from multiple action samples, significantly reducing computational load and time, thereby enhancing overall efficiency. These innovative designs in dataset construction and template generation algorithms support the real-time applicability of the intelligent rehabilitation action evaluation system. (3) Finally, in order to solve problems such as human body differences, environmental interference, motion changes, and sensor errors, this article takes innovative measures in data processing, action segmentation, and feature extraction. We use segmented cubic spline interpolation and moving average to eliminate environmental interference and propose an action segmentation strategy based on action feature analysis to manage random pauses and repeated actions effectively. By constructing human body key-point vectors and extracting angle and distance features, the impact of individual differences and sensing errors is reduced.