# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 1 Introduction  
The coal mine underground personnel position monitoring system [1] is an integral component of safety hedge, aimed at tracking the location of underground workers. This system encompasses various functionalities, including the management and retrieval of entry and exit times for card-carrying personnel and access times to key and restricted areas. Of these functionalities, identity recognition plays a critical role in the system's operation. Fingerprint and face recognition technologies are commonly employed in underground coal mines for this purpose. However, the underground environment presents numerous challenges, such as high levels of coal dust, limited visibility, confined spaces, elevated temperatures, and high humidity. These factors adversely affect the clarity of fingerprints and faces, consequently impacting identification accuracy. Human identity recognition typically relies on one or more internal physiological characteristics [2; 3; 4] (such as fingerprints, iris patterns, palm lines, and other external body features) or behavioral characteristics [5; 6; 7] (such as typing style, gait, voice rhythm, and other behavioral patterns). Among these, gait recognition is an emerging biometric technology that offers several advantages over fingerprint and face recognition, including non-invasiveness, long-range identification capability, minimal environmental influence,and difficulty in disguising. Traditional gait recognition research often involves the use of cameras [8; 9], acceleration sensors [10], and ground sensors [11] to capture movement information. However, these devices are susceptible to issues such as illumination variations, smoke interference, and physical obstacles, which significantly limit their effectiveness in harsh mining environments. In recent years, there has been significant interest in human behavior sensing using wireless signals. This sensing technique is based on the principle that human activities impact the propagation of wireless signals, leading to distinct patterns of signal variations. By establishing the relationship between these signal variation patterns and human activities, it becomes possible to determine the state of human activity. This wireless-based approach offers several advantages over sensor-based gait recognition methods. It does not rely on optimal lighting conditions or the use of specialized sensor devices, making it particularly suitable for the challenging underground environment of coal mines characterized by low visibility, high levels of dust, and humidity. Currently, gait recognition achieved through the collection of WiFi Channel State Information (CSI) demonstrates high accuracy. However, its sensing range is limited, making it unsuitable for long-distance roadway applications in underground environments. In order to address the aforementioned challenges, this research paper introduces the utilization of long-range radio (LoRa) signals for gait recognition, representing the first known application of LoRa signals in this context. In comparison to the wireless signals discussed earlier, LoRa signals offer distinct advantages, including extended propagation distance and enhanced penetration capabilities. These characteristics make LoRa signals particularly well-suited for long-distance wireless sensing in underground environments. The propagation range of LoRa signals can reach up to 10 km in rural areas and several kilometers in urban areas [12; 13]. Furthermore, LoRa terminals are cost-effective, with prices ranging from USD 5 to 15 per unit. They also boast a battery life exceeding ten years, making them highly durable. LoRa technology has already been widely deployed in numerous Asian and European countries, adding to its feasibility and accessibility for practical implementation. Gait recognition based on LoRa signals faces the following key challenges: (1) Gait recognition based on other wireless signals has achieved certain research results, but we adopt LoRa signals for gait recognition for the first time.