# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 4 Discussion
### Related Work
#### 4.1.3 Gait Recognition Based on Wireless Signal  
With the popularity of WiFi devices, wireless sensing based on WiFi signals has made some achievements in the field of gait recognition due to its advantages of non-contact, easy deployment, non-invasive and unaffected by lighting conditions. [PERSON] et al. [22] proposed a user authentication scheme based on deep learning by analyzing the disturbance characteristics of human daily activities on WiFi signal CSI data, and identified 11 targets in walking and stationary states with an accuracy of 94% and 91%, respectively. [PERSON] et al. [23] proposed the WiFiU method to extract principal components from CSI through principal component analysis (PCA). Short-time Fourier transform (STFT) was used to convert PCA components into spectral graphs, from which gait features were extracted. The support vector machine (SVM) algorithm was used for matching to realize identity recognition, with the highest accuracy of 93.5%. [PERSON] et al. [24] proposed the WiWho method to pre-process signals through long-distance multipath elimination and Butterworth bandpass filter. After extracting gait features from CSI, the decision tree algorithm was used for identity recognition. A 92-80% accuracy was achieved in the scenario of 2-6 people. [PERSON] et al. [25] proposed the WiFi-ID method to calculate the time-domain and frequency-domain features of CSI data by using the RelieF feature extraction algorithm. The sparse approximation-based classification (SAC) algorithm was used to identify the target, and the accuracy was 93-77% in the case of 2-6 people. [PERSON] et al. [26] proposed the FreeSence method,combined with principal component analysis (PCA), discrete wavelet transform (DWT) and dynamic time warping (DTW) algorithm; CSI data were processed. In the case of 2-6 people, the recognition accuracy rate was 94.5-88.9%.  
In recent years, millimeter wave radar technology has made good progress in the field of gait recognition because of its high bandwidth, accurate spatial resolution, and strong robustness to the environment. [PERSON] et al. [27] used CNN to extract micro-Doppler (MD) gait features to realize gait recognition. In the scenario of five people, the classification error rate of this method on the verification set was 24.70%. The classification error rate on the test set was 21.54%. [PERSON] et al. [28] proposed the mID method, which is based on a millimeter wave radar point cloud for gait recognition for the first time. LSTM was used to extract the time series features of gait point cloud data, and the recognition accuracy rate reached 89% in the scenario of 12 people. [PERSON] et al. [29] established the first publicly available millimeter wave radar gait point cloud dataset MmGait, and proposed the MmGaitNet method. The method could achieve 90% accuracy in single-person recognition, but the pedestrian path was a fixed path, and the method only achieved 45% accuracy in a random path. [PERSON] et al. [30] proposed the MTPGait method, constructed and released a millimeter wave radar 3D point cloud data set, and designed a neural network based on CNN+LSTM to extract the multi-scale spatio-temporal characteristics of the 3D point cloud in the spatial and temporal dimensions. The results show that under the single scene with any path, MTPGait is able to achieve a recognition accuracy rate of 96.7%, and 90.2% when two people coexist.