# Evaluation of Human Action Based on Feature-Weighted Dynamic Time Warping
## 6 Numerical Experiments  
1. Preliminary (1) Experimental environment: In this paper, we used a computer device with CPU model Intel i5-10400 (Intel, Santa Clara, CA, USA), a graphics card RTX 2070, and 16G RAM; we used Azure Kinect sensors (Microsoft, Redmond, WA, USA) to collect motion data, C++ to write the program and Visual Studio 2017 to compile it. Since Azure Kinect is no longer commercially available, it can be replaced with a depth camera. (2) Data acquisition: Self-constructed healthy person action datasets, which include four types of daily activity actions, i.e., drinking water, combing hair, touching the opposite shoulder, and touching the back pocket, were used; each action type involved 10 healthy subjects to conduct the experiment, where each person completed the action process once in a natural state, resulting in a total of 40 sets of action data. Among the 10 groups of data for each type of action, 4 groups were randomly selected to generate templates for each type of action, and the remaining 6 groups were used as test groups. In the self-constructed patient action datasets, each type of action included experimental data completed by three patients, each of whom completed the action once in their natural state, and a total of 12 sets of action data were obtained.
2. Action segmentation By setting the window length \(w=10\) and the sliding step \(l=1\), we calculated the three-axis difference sum and variance sum of the hand joints through the sliding window. We set the V-value curve peak-peak threshold \(t_{\mathrm{v}}\) as the average of the sequence data, the E-value curve data threshold \(t_{\mathrm{E}}=10\), the window length offset value \(m_{\mathrm{s}}=1.5w\), and \(m_{\mathrm{e}}=3w\) for the segmentation of the four kinds of actions of healthy people and patients. Figure 10 shows the results for the drinking action as an example.  
In the healthy person's action, there are two peaks in the drinking action, where the first peak is shifted forward from the start-point window value and the last peak is shifted  
Figure 10: Segmentation of drinking action in healthy people (**a**) and patients (**b**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Score** & **Similarity** & **Action** \\ \hline
4 & 0.8–1.0 & Completion of action independently \\
3 & 0.6–0.8 & Completion of action slowly but independently \\
2 & 0.4–0.6 & No guardian or assistive devices required to assist in movement \\
1 & 0.2–0.4 & Assistance of guardian or assistive device required \\
0 & 0.0–0.2 & Unable to perform action \\ \hline \hline \end{tabular}
\end{table}
Table 3: Five-point action achievement score mechanism.  
backward from the end-point window value, to obtain the start point and end point of the action, respectively. There are two to three trajectory peaks and some small trajectory peaks in the patients' actions, which are caused by abnormal hand actions and the limb fluttering of the patient, indicating that it is more difficult for the patient to carry out actions with a large spatial span. The results show that the method used in this experiment can better segment the start and end points of the action and has good practicality and universality.