基于LoRa设备的人体活动识别研究

Human activity recognition based on LoRa devices

近年基于LoRa设备建立的诸多传感模型验证了LoRa设备的长距离传感潜力,但使用特征模糊的LoRa无线信号识别人体活动仍然需要进一步研究。分析了LoRa信号受人体活动影响的传播规律,提出了一种LoRa信号处理方法来提取信号变化特征。随后采集数据创建了2个记录人体活动的LoRa数据集,通过当前先进的深度学习网络检验所提方法的效果。对1个房间内活动种类、活动人员,4个房间内活动人员、活动发生房间的识别准确率均达到了90%以上,对比使用卷积循环神经网络直接进行训练的方法也更节省时间和空间资源。

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 proposed 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.