Recognizing activities of daily living from UWB radars and deep learning

Since years, the number of seniors increases while, at the same time, we observe a diminution of the potential support ratio. In order to overcome this limitation, solutions emerged, such as smart homes and wearable devices. Smart homes integrate sensors, actuators, and artificial intelligence to assist seniors in their everyday life. One of the objectives is to recognize the activities of everyday life. This recognition aims to provide the right assistance at the right moment and gives some autonomy to seniors. However, it is a complex task (a significant quantity of different sensors, hardware implementation), and the number of solutions (combinations between approaches, for example, video-based HAR and wearable sensors-based HAR) that exist is important. In this paper, we propose to perform the activity recognition from three ultra-wideband (UWB) radars, deep learning models, and a voting system. Also, all the experiments have been conducted in a real apartment and are composed of 15 different activities. The presented solution is simple compared to the literature since we exploit only one type of sensor. Finally, we obtained promising results with our approach. Indeed, the classification rate reaches 90% and more in some cases.