UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars

Human Activity Recognition (HAR) is becoming increasingly important in smart homes and healthcare applications such as assisted-living and remote health monitoring. In this paper, we use Ultra-Wideband (UWB) and commodity WiFi systems for the passive sensing of human activities. These systems are based on a receiver-only radar network that detects reflections of ambient Radio-Frequency (RF) signals from humans in the form of Channel Impulse Response (CIR) and Channel State Information (CSI). An experiment was performed whereby the transmitter and receiver were separated by a fixed distance in a Line-of-Sight (LoS) setting. Five activities were performed in between them, namely, sitting, standing, lying down, standing from the floor and walking. We use the high-resolution CIRs provided by the UWB modules as features in machine and deep learning algorithms for classifying the activities. Experimental results show that a classification performance with an F1-score as high as 95.53% is achieved using processed UWB CIR data as features. Furthermore, we analysed the classification performance in the same physical layout using CSI data extracted from a dedicated WiFi Network Interface Card (NIC). In this case, maximum F1-scores of 92.24% and 80.89% are obtained when amplitude CSI data and spectrograms are used as features, respectively.