In recent years,many sensor models based on LoRa devices have verified the long-distance sensing potential of LoRa devices,but the use of feature-blurred LoRa wireless signals to identify human activities still requires further research.This paper analyzes the propagation law of LoRa signals affected by human activities,and proposes a LoRa signal processing method to extract signal change features.Subsequently,data are collected to create two LoRa datasets that record human activities,and the pro-posed method is tested through advanced deep learning models.The accuracy of recognizing activity types,activity roles in a room,activity roles,and activity rooms in four rooms reaches over 90％.Compared to the method of using convolutional recurrent neural networks for direct training,it is also more time-saving and spatial resource-saving.
wireless sensinglong distance sensinghuman activity recognitionlora signal feature ex-tractiondeep learning
万方数据
维普
