Fine-grained occupant activity monitoring with Wi-Fi channel state information: Practical implementation of multiple receiver settings

Abstract

Human activity recognition is essential for various smart-home applications. With the development of sensing technology, various approaches have been proposed for occupancy monitoring indoors. However, such approaches have practical limitations that they require additional occupancy sensors, which may raise privacy issues and obtrude on occupants' daily lives. In this research, a Wi-Fi-based occupancy monitoring system, Wi-Sensing, is proposed to recognize occupant's activities of daily living in a non-intrusive way by exploiting commercial off-the-shelf Wi-Fi devices. Channel State Information (CSI) has been extracted from Wi-Fi signals collected from multiple Wi-Fi devices, which could be replaced by Internet of Things (IoT) devices. While multiple receivers are needed to cover the entirety of an indoor space, previous approaches have been proposed to extract numerous features from a single transmitter-receiver pair. In this context, this study presents a new approach toward extracting spatial-temporal features from multiple receivers deployed throughout an indoor space. In this approach, a Short-Time Fourier Transform (STFT) was used to convert time-series CSI data into image data. The converted image data from each receiver was then integrated as large image data, which preserved the temporal-spatial information of all the receiver data. A Convolutional Neural Network (CNN) was used as a feature extractor for the image data, and Long Short-Term Memory (LSTM) was exploited to classify basic activities in daily life (e.g., personal hygiene, eating, mobility, etc.). Wi-Sensing provides over 96% classification accuracy in two different indoor environments.