# Monitoring of [PERSON]'s Disease Based on Wireless Sensing Technology
## 6 Result and Discussion  
The confusion matrix of each classification algorithm is shown in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **A Formula to Calculate** \\ \hline Mean value & \(Y_{MV}=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}\) \\ Standard deviation & \(Y_{SD}=\sqrt[2]{\frac{1}{N-1}\sum\limits_{i=1}^{N}(x_{i}-Y_{MV})^{2}}\) \\ Root mean square & \(Y_{RMS}=\sqrt[2]{\frac{1}{N}\sum\limits_{i=1}^{N}{x_{i}}^{2}}\) \\ Peak-to-peak value & \(Y_{PPV}=\max(x_{i})-\min(x_{i})(i=1,2,\ldots,N)\) \\ Kurtosis & \(Y_{K}=\frac{\frac{1}{N}\sum\limits_{i=1}^{N}\left\|\begin{array}{c}x_{i}\\ x_{i}\\ Y_{RMS}\end{array}\right\|^{4}}{Y_{RMS}\text{ }^{4}}\) \\ Skewness & \(Y_{S}=\frac{\frac{1}{N}\sum\limits_{i=1}^{N}\left\|\begin{array}{c}x_{i}\\ x_{RMS}\end{array}\right\|^{3}}{Y_{RMS}\text{ }^{3}}\) \\ Peak factor & \(Y_{P}=\frac{\max(x_{i})}{\max(x_{i})}\) (\(i=1,2,\ldots,N\)) \\ Waveform factor & \(Y_{W}=\frac{N+Y_{RMS}}{\sum\limits_{i=1}^{N}\left|\begin{array}{c}x_{i}\\ x_{i}\end{array}\right|}\) (\(i=1,2,\ldots,N\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time domain features. Figure 7: The waveforms of each action after data preprocessing: (**a**) normal standing; (**b**) standing of [PERSON]’s disease (HD); (**c**) normal sitting; (**d**) sitting of HD; (**e**) normal walking; (**f**) gait of HD. The experimental accuracy of each algorithm is shown in Figure 8.  
Figure 7a-d shows different waveforms of normal people and patients with HD under static action. It can be seen from the figure that the static action signal waveform of normal people is relatively gentle, while the static action signal waveform of patients fluctuates greatly because patients with HD have convulsions when they are ill and dance-like movements when they are serious. Figure 7e,f shows the signal waveforms of normal gait and gait in patients with HD. Due to the large amplitude of walking itself, the abnormal body swing of patients with HD may be covered by the walking movement, resulting in the signal waveform discrimination between normal gait and abnormal gait not being very obvious, which is consistent with reality. At the same time, we can see from Figure 7 that the differences between the action signal waveform of normal people and patients with HD are obvious in the time domain. In order to reduce the amount of data and improve the efficiency of the algorithm training model, we extracted eight time domain features from the samples, which can well describe the time domain waveform, and the experimental results also prove this. Table 4 is the confusion matrix of SVM and RF. It can be seen that the SVM algorithm can completely distinguish the static actions of patients with HD, while the normal static actions have misclassification, which shows that the performance of patients in the static actions is inconsistent, the performance of body convulsion or dance is different, and the performance of normal people in static actions are consistent. It can also be seen that RF can completely distinguish between normal gait and normal sitting action. Both SVM and RF cannot distinguish the normal gait and the abnormal gait of patients with HD completely, because the time domain signal waveform of them is similar. Figure 8 shows the prediction accuracy of the two algorithms. The prediction accuracy of SVM is 98.0%, and that of RF is 96.7%. Both algorithms can achieve high prediction accuracy, which proves that the experimental scheme described in this paper is feasible. At the same time, we can draw a