A Novel WiFi-Based Personnel Behavior Sensing With a Deep Learning Method

Abstract

People’s activity recognition technology has a large number of applications in indoor monitoring, smart healthcare, and smart homes. Traditional methods require hardware-mounted or wearable sensors, which bring additional costs and impose many limitations on usage, while WiFi devices have the advantages of low cost, wide coverage, and wall penetration, which can compensate for the limitations of traditional means. A WiFi-based behavior sensing system is proposed in this study. As the characteristics of personnel activities would cause path changes, the path decomposition algorithm is designed to use the path information as the feature of the action and to fully exploit the feature information to improve the accuracy of recognition, the system proposes the BI-AT-GRU network in which bi-directional and attention mechanism are added to the gated loop unit to achieve action recognition. The system is evaluated in different environments with different levels of multipath reflections and compared with other systems. The experimental results show that the recognition accuracy of this system in the two scenes is 97.4% and 93.3%, respectively.

Keywords

• Personnel behavior identification
• channel state information
• phase decomposition
• deep learning