LoGait: LoRa Sensing System of Human Gait Recognition Using Dynamic Time Warping

Vision-based gait analysis and human identification systems have been widely proposed in the literature. However, these systems cannot be readily applied in many real-time applications due to involved challenges such as video quality, occlusion, and serious privacy concerns. To overcome such issues, we propose the LoGait system that leverages ubiquitous LoRa signals recognize gait in different indoor environments. Our work is based on the intuition that the walking pattern of different users can be distinguished by distinct stride size and frequency. The wireless LoRa signal which is interfered by human walking will capture the gait information of subjects. In combination with the long-distance transmission ability of LoRa signal, the system enables a larger sensing range of gait recognition compared to the WiFi-based gait recognition system. The proposed LoGait system utilizes the phase difference between two LoRa receiver channels, along with a set of filtering techniques, to extract distinctive features and generate a human gait profile. This profile is then matched against a database using a dynamic time warping (DTW)-based recognition algorithm, enabling accurate identification based on unique gait patterns. It has been validated in three different scenarios for gait recognition namely line of sight (LOS), non-LOS (NLOS), and long distance, with an accuracy of 85.13%, 79.14%, and 84.14%, respectively.