HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data. (11th January 2018)

• Record Type: Journal Article
• Authors: Guo, Linlin; Wang, Lei; Liu, Jialin; Zhou, Wei; Lu, Bingxian
• Other Names: Zhang Kuan Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2018(2018)
• Publication Date: 2018-01-11
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2018/6163475
• Languages: English
• ISSNs: 1530-8669

Abstract

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 93 % using WiAR dataset.