LoRa-based Human Activity Recognition Using Deep Learning

Wireless sensing combined with deep learning has become a research hotspot in recent years, but most of the existing work is limited by the sensing distance. The establishment of several sensing models in recent years has proven the long-distance sensing ability of LoRa devices, but there is still insufficient work on how to combine LoRa devices with deep learning. This paper analyzes the propagation and variation laws of LoRa signals affected by human activities, proposes a LoRa signal processing method to extract features suitable for deep learning networks. By comparing current advanced models with commonly used signal processing methods, the final result is good. For the types and roles of activities in one room, the accuracy exceeds 94%. Compared with common signal processing methods, it also saves space and time resources, which proves the effectiveness of the proposed method.