AAH: accurate activity recognition of human beings using WiFi signals. (23rd December 2015)

• Record Type: Journal Article
• Authors: Gu, Yu; Quan, Lianghu; Ren, Fuji
• Other Names: Higuera‐Toledano M. Teresa guestEditor.; Brinkschulte Uwe guestEditor.; Rettberg Achim guestEditor.; Qiang Weizhong guestEditor.; Zheng Xianghan guestEditor.; Hsu Ching‐Hsien guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 14(2016)
• Page Start: 3910
• Page End: 3926
• Publication Date: 2015-12-23
• Subjects: accurate activity recognition -- WiFi signals -- fusion algorithm; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3741
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The flourishing social networks nowadays have greatly enriched our ways of communications and thus brought people in the world much closer than ever. However, critical contexts of the traditional face‐to‐face communications, for example, body gestures, could be missing during the online communication, hampering the user experiences. To fill in the blank, this paper presents a passive and devices‐free activity recognition system, by harvesting fingerprints of different activities from ubiquitous WiFi signals. It can be integrated into any existing WLAN networks without additional hardware supports. Also, it does not need the subjects to be cooperative during the recognition process. A prototype system is built and evaluated via extensive real‐world experiments. By comparing with three state‐of‐the art solutions, that is, K‐nearest neighbor, naive Bayes, and bagging, we show the superiority of the proposed method in terms of accuracy and complexity. Copyright © 2015 John Wiley & Sons, Ltd.