## 1 Introduction
[22] proposed developing a real-time, simple and high-accuracy fall detection system for the elderly using 3-DOF accelerometers, for which the fall detection algorithm compares the acceleration with the lower fall threshold and upper fall threshold values to accurately detect a fall event; in addition, a post-fall recognition module was added to the method to enhance the performance and accuracy, and the system achieves 100% sensitivity and accuracy. Nevertheless, our research focused on identifying different fall postures, and the threshold method can easily distinguish falls from daily behaviors, but it cannot effectively identify different fall postures. This fall detection method protects users' privacy and has small restrictions on the scope of applications, which is an ideal fall detection technology. Various sensors on wearable devices provide essential data for fall posture detection. However, the high-dimensional nonlinear data collected by multiple sensors is difficult to process and classify. Therefore, the first but most important thing that needs to be done is to extract features from massive sample data. Most researchers' feature extraction methods mainly include time domain analysis, frequency domain analysis, and time-frequency domain analysis. The time domain and frequency domain analysis methods mainly extract eigenvalues from the signal's time and frequency domain information. Commonly used eigenvalues mainly include mean, standard deviation, variance, root mean square, and peak-to-peak values. For example, [PERSON] et al. [23] used the root mean square of acceleration and angular velocity for fall detection. [PERSON] et al. [24] extracted feature values such as the mean, variance, maximum, and minimum to train a fall detection model. The above methods had a shorter calculation time and lower space complexity, but it cannot fully express the original signal's information and cannot handle non-stationary signals well. The most commonly used time-frequency analysis method was the wavelet transform, which was suitable for analyzing non-stationary signals. Wavelet transform only further decomposes the low-frequency part of the signal and does not decompose the high-frequency part. Compared with the wavelet transform, the wavelet packet transform (WPT) can analyze the signal more finely and decompose the signal's high-frequency part, which is widely used in feature extraction [25; 26; 27; 28]. Therefore, the wavelet packet transform can be applied to fall posture recognition to improve the entire system's recognition effect. Moreover, fall postures can be classified using pattern recognition algorithms, which are good at digging out characteristic parameters that can distinguish different behaviors. Traditional classifiers such as artificial neural networks (ANNs), K-nearest neighbor (KNN) all require many samples to achieve good classification results, and their computational cost is high. At the same time, the processor in the wearable device cannot provide enough memory. However, for fall posture recognition cases, the number of fallen samples is limited, making it challenging to achieve a high recognition accuracy. The support vector machine (SVM) algorithm has excellent advantages in solving small sample and local extreme value problems. Even if the number of samples is limited, it can also achieve good classification results. Compared with ANN, SVM has a low computational cost and small memory occupation and is easy to implement in wearable devices. [PERSON] et al. [29] presents the research and simulation of wearable device-based fall detection approach by addressing the building of wearable device-based fall detection system for elderly care by using mobile devices. The findings suggest that SVM with the polynomial (order 5) method, which achieved 68.91% overall accuracy and produced only 24.46% FPR, is the most precise model for the fall detection system. [PERSON] et al.