Rapid: a multimodal and device-free approach using noise estimation for robust person identification

Yuanying Chen, Wei Dong, Yi Gao, Xue Liu, Tao Gu

Research output: Contribution to journalArticlepeer-review

Abstract

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 Rapid, 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. We implement low-cost Rapid nodes and evaluate our system using experiments at multiple locations with a total of 1800 gait instances from 20 volunteers, and the results show that Rapid identifies a subject with an average accuracy of 92% to 82% from a group of 2 to 6 subjects, respectively.
Original languageEnglish
Article number41
Number of pages27
JournalACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume1
Issue number3
DOIs
Publication statusPublished - Sep 2017
Externally publishedYes

Keywords

  • Multimodal person identification
  • Channel State Information (CSI)
  • audio sensing
  • noise estimation

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