The global rise in the elderly population has increased the demand for effective fall detection in Ambient Assisted Living (AAL) environments. This paper introduces a novel and reliable fall detection system utilizing frequencymodulated continuous wave (FMCW) radar, designed to address privacy concerns, operate reliably in low-light conditions, and provide ease of installation. Data from two wall-mounted radars capture a variety of activities, including simulated falls, across five configurations to enhance model generalizability. Radar data processing employs the Fast Fourier Transform (FFT) and the Capon algorithm to generate Range-Azimuth and Range-Elevation maps, which serve as input features for a proposed 3D Convolutional Neural Network (3D CNN) model. This model achieves an accuracy of 94.33% and F1-score of 93.5%, combining high performance with adaptability across diverse environments and user needs. This work provides a robust solution for fall detection with significant potential for deployment in real-world elderly care settings.

Fall Detection in Ambient-Assisted Living Environments Using FMCW Radars and Deep Learning / Fard, A. S.; Mashhadigholamali, M.; Zolfaghari, S.; Abedi, H.; Chakraborty, M.; Karmani, S.; Borzi', L.; Daneshtalab, M.; Shaker, G.. - ELETTRONICO. - (2025), pp. 1-6. (Intervento presentato al convegno 2025 IEEE International Radar Conference (RADAR) tenutosi a Atlanta, Georgia (USA) nel 3-9 May, 2025) [10.1109/RADAR52380.2025.11031826].

Fall Detection in Ambient-Assisted Living Environments Using FMCW Radars and Deep Learning

Borzi' L.;
2025

Abstract

The global rise in the elderly population has increased the demand for effective fall detection in Ambient Assisted Living (AAL) environments. This paper introduces a novel and reliable fall detection system utilizing frequencymodulated continuous wave (FMCW) radar, designed to address privacy concerns, operate reliably in low-light conditions, and provide ease of installation. Data from two wall-mounted radars capture a variety of activities, including simulated falls, across five configurations to enhance model generalizability. Radar data processing employs the Fast Fourier Transform (FFT) and the Capon algorithm to generate Range-Azimuth and Range-Elevation maps, which serve as input features for a proposed 3D Convolutional Neural Network (3D CNN) model. This model achieves an accuracy of 94.33% and F1-score of 93.5%, combining high performance with adaptability across diverse environments and user needs. This work provides a robust solution for fall detection with significant potential for deployment in real-world elderly care settings.
2025
979-8-3315-3956-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3001559