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Related Experiment Video

Updated: Jul 16, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

WAD-YOLO: A Lightweight Fall Detection Algorithm for Visual Sensor Systems Based on Wavelet Transform and Dynamic

Zhongyu He¹, Fenghua Zhu², Shengli Duan¹

¹School of Rail Transportation, Shandong Jiaotong University, Jinan 250300, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

This study introduces WAD-YOLO, an efficient algorithm for fall detection using visual sensors. It improves accuracy and significantly reduces model size for deployment on edge devices.

Keywords:

YOLOv11 dynamic convolution edge deployment fall detection lightweight network visual sensor system wavelet transform

Related Experiment Videos

Last Updated: Jul 16, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Computer Vision
Artificial Intelligence
Biomedical Engineering

Background:

Falls in the elderly are a major public health concern.
Camera-based systems offer non-intrusive fall monitoring.
Edge device limitations hinder accurate fall detection.

Purpose of the Study:

To develop an efficient and lightweight fall detection algorithm for visual sensor systems.
To address the trade-off between model complexity and performance on resource-constrained edge devices.

Main Methods:

Proposed WAD-YOLO algorithm utilizing wavelet transform convolution (WTConv) for feature extraction.
Incorporated dynamic upsampling (DySample) and adaptive downsampling (ADown) modules.
Evaluated on the public Fall Detection dataset.

Main Results:

WAD-YOLO increased precision by 3.8% and mAP50 by 3.7% compared to YOLOv11n.
Reduced parameter count by 3.0 × 10^5.
Maintained comparable GFLOPs, indicating efficiency.

Conclusions:

WAD-YOLO offers a promising solution for lightweight, high-accuracy fall detection on edge sensor platforms.
The algorithm effectively captures fall patterns while minimizing computational load.