title: A Robust and Device-Free System for the Recognition and Classification of Elderly Activities.
abstract: Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise.
authors.affiliation: Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China. lifangmin@whut.edu.cn.|School of Information Engineering, Wuhan University of Technology, Wuhan 407003, China. lifangmin@whut.edu.cn.
authors.forename: Fangmin
authors.initials: F
authors.lastname: Li
authors.affiliation: School of Information Engineering, Wuhan University of Technology, Wuhan 407003, China. alqaness@whut.edu.cn.
authors.forename: Mohammed Abdulaziz Aide
authors.initials: MA
authors.lastname: Al-Qaness
authors.affiliation: School of Information Engineering, Wuhan University of Technology, Wuhan 407003, China. zhang_yong@whut.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China. bihaizhao@163.com.
authors.forename: Bihai
authors.initials: B
authors.lastname: Zhao
authors.affiliation: Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China. Xidaoluan@ccsu.cn.
authors.forename: Xidao
authors.initials: X
authors.lastname: Luan
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D000465:Algorithms; D015374:Biosensing Techniques / Q000379:methods*; D006801:Humans; D020407:Internet; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D059015:Wireless Technology
keywords: Principle Component Analysis; Wi-Fi; activity recognition; device-free; feature extraction