## 2 Method
### Data Preprocessing
Table 6 compares the recognition accuracy, recall, f1 score, precision, training time, and real-time decision time of the algorithm before and after the random forest feature selection. The classifier used is a support vector machine with a linear kernel function. We can observe that after feature selection through the random forest, the algorithm's accuracy, recall, f1 score and precision are all further improved, and the training time and real-time decision time are also significantly shortened. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Linear\_SVM**} & \multicolumn{1}{c}{**Rbf\_SVM**} & \multicolumn{1}{c}{**Xgboost**} & \multicolumn{1}{c}{**Logistic Regression**} & \multicolumn{1}{c}{**KNN**} \\ \hline \multirow{2}{*}{Accuracy} & without WPT & 0.967 & 0.939 & 0.936 & 0.941 & 0.934 \\  & with WPT & 0.987 & 0.974 & 0.978 & 0.973 & 0.965 \\ \hline \multirow{2}{*}{Recall} & without WPT & 0.955 & 0.932 & 0.930 & 0.936 & 0.902 \\  & with WPT & 0.973 & 0.938 & 0.952 & 0.940 & 0.935 \\ \hline \multirow{2}{*}{f1 score} & without WPT & 0.954 & 0.951 & 0.939 & 0.940 & 0.913 \\  & with WPT & 0.974 & 0.955 & 0.960 & 0.945 & 0.933 \\ \hline \multirow{2}{*}{Precision} & without WPT & 0.953 & 0.973 & 0.949 & 0.947 & 0.927 \\  & with WPT & 0.977 & 0.975 & 0.967 & 0.952 & 0.935 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods: accuracy, recall, f1 score and precision on different classifiers with or without WPT. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Behavior Description** & **Type** \\ \hline
903 & from vertical falling down on the knees & \\
904 & from vertical falling down on the knees and then lying on the floor & front-knees \\ \hline
910 & from vertical falling on the floor, ending lying & \\
911 & from vertical falling on the floor, ending lying in right lateral & back-lying \\  & position & \\
912 & from vertical falling on the floor, ending lying in left lateral position & \\ \hline
909 & from vertical falling on the floor, ending sitting & back-sitting \\ \hline
915 & from vertical falling on the floor, ending lying & fall to the left \\ \hline
913 & from vertical falling on the floor, ending lying & fall to the right \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Linear\_SVM** & **Rbf\_SVM** & **Xgboost** & **Logistic Regression** & **KNN** \\ \hline \multirow{2}{*}{Accuracy} & without WPT & 0.967 & 0.939 & 0.936 & 0.941 & 0.934 \\  & with WPT & 0.987 & 0.974 & 0.978 & 0.973 & 0.965 \\ \hline \multirow{2}{*}{Recall} & without WPT & 0.955 & 0.932 & 0.930 & 0.936 & 0.902 \\  & with WPT & 0.973 & 0.938 & 0.952 & 0.940 & 0.935 \\ \hline \multirow{2}{*}{f1 score} & without WPT & 0.954 & 0.951 & 0.939 & 0.940 & 0.913 \\  & with WPT & 0.974 & 0.955 & 0.960 & 0.945 & 0.933 \\ \hline \multirow{2}{*}{Precision} & without WPT & 0.953 & 0.973 & 0.949 & 0.947 & 0.927 \\  & with WPT & 0.977 & 0.975 & 0.967 & 0.952 & 0.935 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods: accuracy, recall, f1 score and precision on different classifiers with or without WPT. #### 3.2.3 Comparisons with Existing Methods

In addition, in order to verify the effectiveness and superiority of the method in this paper, comparisons were made with several existing excellent methods on \"Simulated Falls and Daily Living Activities Data Set\". Results are as shown in Table 7. The experimental results show that the accuracy of the method proposed in this paper is better than that of the other three methods. ### Discussions

#### 3.3.1 Discussion of the Limitation

Although the method proposed in this paper can effectively recognize the posture of a fall, it requires the wearing of many sensors. We need to consider the comfort of wearing these sensors for the elderly.