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Wireless Communications and Mobile Computing
Volume 2018, Article ID 6163475, 15 pages
https://doi.org/10.1155/2018/6163475
Research Article

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software, Dalian University of Technology, Dalian, China

Correspondence should be addressed to Lei Wang; gro.eeei@gnaw.iel

Received 18 September 2017; Accepted 27 November 2017; Published 11 January 2018

Academic Editor: Kuan Zhang

Copyright © 2018 Linlin Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than using WiAR dataset.