Design And Implementation Of A Multimodal And Device-free System For Person Identification

In recent years,device-free human sensing is a key technology to support many applications such as indoor navigation and activity recognition.By exploiting WiFi signals reflected by human body,there have been many WiFi-based device-free human sensing applications.Among these applications,person identification is a fundamental technology to enable user-specific services.In this paper,we present a system that can perform robust person identification in a device-free and low-cost manner,using fine-grained channel information(i.e.,CSI)of WiFi and acoustic information from footstep sound.In order to achieve high accuracy in real-life scenarios with both system and environment noise,we perform noise estimation and include two different confidence values to quantify the impact of noise to both CSI and acoustic measurements.Based on an accurate gait analysis,we then adaptively fuse CSI and acoustic measurements to achieve robust person identification.This paper makes the following contributions.1.This paper proposes a quantitative approach to estimate noise from both CSI and acoustic signals,and uses the estimated confidence values to perform robust person identification.2.This paper designs and implements a prototype using a COTS radio chipset and acoustic sensor,incorporating both radio-based sensing and acoustic sensing modalities.3.This paper conducts comprehensive experiments to examine the feasibility and performance of Rapid on a gait database.The results show that Rapid can achieves an overall accuracy of 92%,achieving an overall accuracy improvement of 15%compared with the state-of-the art monomodal approach.