WiLay: A Two-Layer Human Localization and Activity Recognition System Using WiFi

Abstract

Human activity monitoring (HAM) in the home environment has become increasingly important due to its broad applications including elder care, and well-being management. Recently, some state-of-the-art WiFi-based HAM systems have been proposed due to its properties of non-intrusive and privacy-friendly. However, their key drawback lies in ignoring the crucial impact of human position on HAM. To solve this problem, we present a two-layer WiFi-based HAM system (WiLay), which combines human activity recognition (HAR) with indoor human location (IHL) to provide more integrated information for HAM. Specifically, in the first layer, WiLay adopts the high-frequency energy (HFE) feature of WiFi signals to detect human moving. Then, in the second layer, different processing methods are employed for processing different types of motions accordingly. When the subject activities are static (SAs, the activity without position change), e.g., standing and sitting, WiLay locates the subject before recognizing the specific motion. On the contrary, when the activities are the moving activities (MAs), to reduce the loss of motion information, WiLay employs a comprehensive classifier generated by all different subcarrier classifiers voting, to recognize these MAs accurately. Extensive experimental results show that WiLay has high accuracy with a 99.9% SA/MA detection accuracy rate in the first layer, and a 99.7% location accuracy rate with 98.1% recognition performance for SAs and 90.2% recognition performance for MAs in the second layer.