Resumen
This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%.