TinyML for UWB-radar based presence detection

• Published in: 2022 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 8
• Main Authors: Pavan, Massimo, Caltabiano, Armando, Roveri, Manuel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.07.2022
• Subjects: Presence detection; Privacy-preserving computation; Tiny Machine Learning; UltraWideBand (UWB) radar
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN55064.2022.9892925

Tiny Machine Learning (TinyML) is a novel research area aiming at designing machine and deep learning models and algorithms able to be executed on tiny devices such as Internet-of-Things units, edge devices or embedded systems. In this paper we introduce, for the first time in the literature, a TinyML solution for presence-detection based on UltrawideBand (UWB) radar, which is a particularly promising radar technology for pervasive systems. To achieve this goal we introduce a novel family of tiny convolutional neural networks for the processing of UWB-radar data characterized by a reduced memory footprint and computational demand so as to satisfy the severe technological constraints of tiny devices. From this technological perspective, UWB-radars are particularly relevant in the presence-detection scenario since they do not acquire sensitive information of users (e.g., images, videos or audio), hence preserving their privacy. The proposed solution has been successfully tested on a public-available benchmark for the indoor presence detection and on a real-world application of in-car presence detection.