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Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

FPGA Implementation of a Radar-Based Fall Detection System Using Binarized Convolutional Neural Networks.

Hyeongwon Cho¹, Soongyu Kang¹, Yunho Jung^1,2

¹Department of Smart Air Mobility, Korea Aerospace University, Goyang 10540, Republic of Korea.

Sensors (Basel, Switzerland)

|May 4, 2026

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This study introduces a lightweight radar system for rapid fall detection in elderly individuals. The novel approach significantly speeds up processing, enhancing safety and privacy in home monitoring.

Area of Science:

Electrical Engineering
Computer Science
Gerontology

Background:

Increasing elderly population living alone necessitates effective fall detection systems.
Existing systems face challenges with privacy, line-of-sight, continuous monitoring, and high computational complexity.
Need for compact, low-power, and efficient hardware for distributed fall detection.

Purpose of the Study:

To propose a lightweight fall detection system using continuous-wave (CW) radar and a binarized convolutional neural network (BCNN).
To achieve efficient hardware implementation for reduced power consumption and smaller hardware footprint.
To validate the system's accuracy and processing speed for real-time fall detection.

Main Methods:

Utilized continuous-wave (CW) radar sensors for non-invasive monitoring.

Keywords:

binarized convolutional neural network (BCNN)continuous-wave (CW) radar fall detection field-programmable gate array (FPGA)system-on-chip (SoC)

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Applied short-time Fourier transform (STFT) for preprocessing radar signals into binary spectrograms.

Developed a binarized convolutional neural network (BCNN) for activity classification.

Implemented preprocessing and classification modules as hardware accelerators on a field-programmable gate array (FPGA) within a system-on-chip (SoC) architecture.

Main Results:

Achieved 96.1% accuracy in classifying five fall activities and seven non-fall activities.
Hardware acceleration resulted in significant speedups: 387.5× for preprocessing and 86.7× for classification compared to software.
Overall system processing time reduced to 2.58 ms, an 89.5× speedup over the software baseline.

Conclusions:

The proposed lightweight CW radar and BCNN system offers an efficient and accurate solution for fall detection.
Hardware acceleration on FPGA significantly enhances processing speed and reduces power consumption, making it suitable for distributed deployment.
This technology holds promise for improving safety and independence for the elderly living alone.