Device Free Activity Recognition using Ultra-Wideband Radio Communication

Human Activity Recognition (HAR) is a fundamental building block in many Internet of Things (IoT) applications. Although there has been a lot of interest in HAR, research in non-intrusive activity recognition is still in nascent stages. This research investigates the capability of Ultra-Wideband (UWB) communication technology to be used for HAR. In this work, UWB radio devices are placed in the periphery of a monitored area. This setup infers user activities without the need of any additional sensors or physical device. Packets are exchanged between these UWB devices, and received packets are used to obtain information of the environment. The key idea is that these received packets are affected by environmental modification due to the human activities. We collect Channel Impulse Response (CIR) data from the received packets of the UWB signals. We then use machine learning algorithms to classify the activity (standing, sitting, lying) being performed.

The experiments show that by using CIR data as features we can classify simple activities such as standing, sitting, lying and when the room is empty with an accuracy of 95%.To compare this performance, we trained classification models using Wi-Fi Channel State Information (CSI). We found that for all the models UWB CIR significantly outperformed Wi-Fi CSI in activity classification. This study also includes an application for this system. We used the HAR system for caloric expenditure estimation during a time period. We use HAR to infer the pose and time spent at each pose and use models from the literature to estimate the caloric expenditure for each pose. Our approach reports 32% more calories than what is reported by commercial devices, which are known to severely under-report calories when the subjects are not very active.