Gesture Recognition for FMCW Radar on the Edge

This paper introduces a lightweight gesture recognition system based on 60 GHz frequency modulated continuous wave (FMCW) radar. We show that gestures can be characterized efficiently by a set of five features, and propose a slim radar processing algorithm to extract these features. In contrast to previous approaches, we avoid heavy 2D processing, i.e. range-Doppler imaging, and perform instead an early target detection - this allows us to port the system to fully embedded platforms with tight constraints on memory, compute and power consumption. A recurrent neural network (RNN) based architecture exploits these features to jointly detect and classify five different gestures. The proposed system recognizes gestures with an F1 score of 98.4% on our hold-out test dataset, it runs on an Arm Cortex-M4 microcontroller requiring less than 280 kB of flash memory, 120 kB of RAM, and consuming 75 mW of power.Comment: 4 pages, 5 figures, submitted to 2024 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT

Automatic Label Creation Framework for FMCW Radar Images Using Camera Data

This work was supported in part by the Project A-SWARM through the German Federal Ministry of Economy and Industry (BMWI) by the Maritime Forschungsstrategie 2025 under Project 03SX485D.Data acquisition and treatment are key issues for any Deep Learning (DL) technique, especially in computer vision tasks. A big effort must be done for the creation of labeled datasets, due to the time this task requires and its complexity in cases where different sensors must be used. This is the case of radar imaging applications, where radar data are dif cult to analyze and must be labeled manually. In this paper, a semi-automatic framework to generate labels for range Doppler maps (radar images) is proposed. This technique is based on a sensor fusion approach with radar and camera sensors. The proposed scheme operates in two steps: The rst step is the environment features extraction, in which the radar data is preprocessed and ltered to remove ghost targets and detect clusters, and camera data are used to extract the information of the targets. In the second step, a rule-based system that considers the extracted features fuses the information to generate labels for the radar data. By using the proposed framework, the experimentation performed suggests that the time required to label the data is reduced as well as the possibility of human error during the labeling task. Our results show that the proposed technique can improve the nal model accuracy with regards the traditional labeling method, carried out by human experts.Project A-SWARM through the German Federal Ministry of Economy and Industry (BMWI) by the Maritime Forschungsstrategie 2025 03SX485