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A Robust and Device-Free System for the Recognition and Classification of Elderly Activities

Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China
School of Information Engineering, Wuhan University of Technology, Wuhan 407003, China
Author to whom correspondence should be addressed.
Academic Editors: Mianxiong Dong, Zhi Liu, Anfeng Liu and Didier El Baz
Sensors 2016, 16(12), 2043;
Received: 25 October 2016 / Revised: 25 November 2016 / Accepted: 29 November 2016 / Published: 1 December 2016
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
PDF [1714 KB, uploaded 1 December 2016]


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. View Full-Text
Keywords: Wi-Fi; activity recognition; device-free; feature extraction; Principle Component Analysis Wi-Fi; activity recognition; device-free; feature extraction; Principle Component Analysis

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Li, F.; Al-qaness, M.A.A.; Zhang, Y.; Zhao, B.; Luan, X. A Robust and Device-Free System for the Recognition and Classification of Elderly Activities. Sensors 2016, 16, 2043.

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