HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data
Joint Authors
Lu, Bingxian
Guo, Linlin
Wang, Lei
Liu, Jialin
Zhou, Wei
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-11
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Guo, Linlin& Wang, Lei& Liu, Jialin& Zhou, Wei& Lu, Bingxian. 2018. HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1216126
Modern Language Association (MLA)
Guo, Linlin…[et al.]. HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1216126
American Medical Association (AMA)
Guo, Linlin& Wang, Lei& Liu, Jialin& Zhou, Wei& Lu, Bingxian. HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1216126
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1216126