Unveiling the Potential of Tiny Machine Learning for Enhanced People Counting in UWB Radar Data

Tiny Machine Learning (TinyML) allows to move the intelligence processing as close as possible to where data are generated, hence reducing the latency with which a decision is made and being able to process data even when remote connection is scarce or absent. In this technological scenario, Ultra-Wideband (UWB) radar data represent a new and challenging source of data providing relevant information, while guaranteeing the privacy of users. This paper introduces a novel TinyML solution able to count the number of people in a given area by processing UWB radar data. This novel solution was carefully designed to guarantee a high counting accuracy, while reducing the memory and computational demand so as to be executed on tiny devices. Experimental results on a real-world UWB radar dataset show the effectiveness of the proposed solution.