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.
Highlights
Human activity recognition has been an important area of human-computer interfaces (HCI)
We present a device-free activity recognition system, which requires only a ubiquitous Wi-Fi router, where the user is free and does not need to wear sensors or be monitored by a camera
We present an efficient Channel State Information (CSI)-Principal Component Analysis (PCA) method to reduce dimensionality and to expose the real trend of the CSI stream that is caused by human motion
Summary
Human activity recognition has been an important area of human-computer interfaces (HCI). A huge amount of literature has been presented in this important search field by using various technologies such as vision-based [3], acoustic-based [4], accelerometer [5], wearable sensors [6], environment installed sensors [7,8], and smartphones [9]. Such approaches depend on a special device that may be considered as the main drawback of these systems, for instance: a vision-based system requires a camera to monitor human activity, which causes a privacy concern for the target people.
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