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Updated: Jul 10, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

A Lightweight Human Fall Detection Network.

Xi Kan¹, Shenghao Zhu², Yonghong Zhang^1,2

¹School of the Internet of Things Engineering, Wuxi University, Wuxi 214105, China.

Sensors (Basel, Switzerland)

|November 25, 2023

Summary

This summary is machine-generated.

A new lightweight fall detection model, CGNS-YOLO, improves accuracy and efficiency for the elderly. It reduces model size and computational load, enhancing hardware adaptability for real-world fall detection systems.

Keywords:

GDCN module GSConv module NAM SIoU YOLOv5 fall detection

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Design and Analysis for Fall Detection System Simplification

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Area of Science:

Computer Vision
Artificial Intelligence
Gerontology

Background:

Aging population presents increased health risks, with falls being a major concern for the elderly.
Existing YOLOv5 fall detection methods face challenges in computational demand, hardware integration, and occlusion handling.

Purpose of the Study:

To develop a lightweight and efficient human fall detection model for the elderly.
To address the limitations of current YOLOv5-based fall detection systems.

Main Methods:

Introduced CGNS-YOLO, a novel lightweight approach reconfiguring the YOLOv5s neck network with GSConv and GDCN modules.
Integrated a normalization-based attention module (NAM) to focus on salient features.
Employed the SIoU loss function for improved convergence and precision.

Main Results:

Achieved a 1.2% enhancement in detection accuracy compared to standard YOLOv5s.
Reduced model parameter count by 20.3% and floating-point operations by 29.6%.
Demonstrated superior efficacy and hardware adaptability through instance analysis and comparative assessments.

Conclusions:

CGNS-YOLO offers a promising solution for effective and efficient elderly fall detection.
The model's lightweight design enhances its suitability for hardware deployment in real-world scenarios.