## 2 Method
### Data Preprocessing
In addition, the method proposed in this paper does not perform learning, training, and recognition for some special fall postures, such as from lying down, rolling out of bed, and walking to the floor, falling down from standing, and slowly slipping on a wall. Therefore, these special behaviors may not be effectively recognized. #### 3.3.2 Discussion on the Future Work

Although the method proposed in this paper has been simulated on a computer and achieved good results, it still needs to be verified in practical applications. Then, we will embed the model trained on the computer into the mobile phone app, use wearable sensors to collect data, and send the data to the mobile phone via Bluetooth. The mobile phone processes the data in real time, determines whether the elderly fell, and recognizes the posture of the fall. Once the elderly has fallen, the mobile phone will immediately send the posture and location information of the fall to family members or medical staff so that the elderly can be rescued in time. ## 4 Conclusions

This paper proposed a fall posture classification and recognition method based on WPT and SVM. To be specific, the algorithm uses the WPT to extract the original signal features, random forest for feature selection, and SVM for classification. The following conclusions can be drawn:

1. The energy ratio of the frequency band obtained after wavelet packet decomposition can be used as features to show the characteristics of the original signal and improve the classifier's recognition effect;
2. Using random forest for feature screening and constructing a new feature set can further improve the accuracy of recognition and shorten the algorithm's training time and real-time decision time; \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Before Feature Selection** & **After Feature Selection** \\ \hline Accuracy & 0.986504 & 0.990553 \\ Recall & 0.972577 & 0.978491 \\ f1 score & 0.974415 & 0.979512 \\ Precision & 0.976582 & 0.981099 \\ Training time & 264.93 (s) & 7.59 (s) \\ Real-time decision time & 4.29 (s) & 0.12 (s) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison before and after feature selection based on Linear_SVM. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline [PERSON] [34] based on one-class SVM & 0.932 \\ [PERSON] [35] based on BP neural network & 0.901 \\ [PERSON] [36] based on SVM & 0.922 \\ Proposed in this paper & 0.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different methods. Compared with other classifiers, by using the linear kernel function the SVM can effectively improve the accuracy of classification, and the recognition effect is the best. Q.Z. ([PERSON]): methodology, software, writing--original draft, writing--review and editing. J.T. : conceptualization, writing--review and editing. Q.S. : review and editing, supervision. X.Z., M.D., Q.Z. ([PERSON]): review and editing, supervision. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant No. 61973172, 61973175, 62003175 and 62003177), the National Key Research and Development Project (Grant No. 2019 YFC1510900), the key Technologies Research and Development Program of Tianjin (Grant No. 19 JCZDJC32800), this project also funded by China Postdoctoral Science Foundation (Grant No. 2020M670633) and Academy of Finland under No. 315660. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are openly available in UCI Machine Learning Repository. The authors declare no conflict of interest. ## References

* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Prevalence, risk factors, circumstances for falls and level of functional independence among geriatric population--A descriptive study. _Indian J. Public Health_**2019**, 63, 21-26. [CrossRef]
* [PERSON] et al.