Exploring LoRa and Deep Learning-Based Wireless Activity Recognition

Abstract

Today’s wireless activity recognition research still needs to be practical, mainly due to the limited sensing range and weak through-wall effect of the current wireless activity recognition based on Wi-Fi, RFID (Radio Frequency Identification, RFID), etc. Although some recent research has demonstrated that LoRa can be used for long-range and wide-range wireless sensing, no pertinent studies have been conducted on LoRa-based wireless activity recognition. This paper proposes applying long-range LoRa wireless communication technology to contactless wide-range wireless activity recognition. We propose LoRa and deep learning for contactless indoor activity recognition for the first time and propose a more lightweight improved TPN (Transformation Prediction Network, TPN) backbone network. At the same time, using only two features of the LoRa signal amplitude and phase as the input of the model, the experimental results demonstrate that the effect is better than using the original signal directly. The recognition accuracy reaches 97%, which also demonstrate that the LoRa wireless communication technology can be used for wide-range activity recognition, and the recognition accuracy can meet the needs of engineering applications.