Related Experiment Video
Updated: May 14, 2026
08:05
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
Radar-Based Fall Detection Using Micro-Doppler Signatures: A Comparative Analysis of YOLO Architectures
Ibrahim Seflek1, Mücahid Barstuğan1
1Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya 42250, Türkiye.
Sensors (Basel, Switzerland)
|May 13, 2026
Summary
This study uses continuous-wave radar and YOLO architecture to detect falls in elderly individuals. The system achieved 100% accuracy in binary fall detection, offering a promising solution for elder safety.
Area of Science:
- Gerontology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Global life expectancy is rising, leading to an increasing elderly population.
- Falls pose a significant health risk to older adults, necessitating effective detection methods.
- Current fall detection systems often lack accuracy or require intrusive sensors.
Purpose of the Study:
- To develop and evaluate a radar-based system for detecting falls and daily activities in elderly individuals.
- To assess the performance of the YOLO architecture for fall detection using micro-Doppler signatures.
- To investigate the generalizability of the proposed method across different subjects and datasets.
Main Methods:
- Collected fall and daily activity data from 10 individuals in home environments using continuous-wave (CW) radar.
- Generated micro-Doppler signatures and augmented the dataset for improved training.
- Applied and compared different YOLO architectures for binary (fall/non-fall) and multi-class (seven activities) classification.
- Validated the model's generalizability using the Leave-One-Subject-Out (LOSO) approach and a public dataset.
Main Results:
- Achieved 100% accuracy for binary fall/non-fall classification.
- Attained 88.02% accuracy for multi-class classification of seven different activities.
- Demonstrated robust generalizability of the YOLO architecture through LOSO validation and public dataset analysis.
Conclusions:
- The YOLO architecture is highly effective for radar-based fall and activity detection in elderly individuals.
- Radar-based systems utilizing YOLO offer a promising, non-intrusive solution for enhancing elder safety.
- The study highlights the potential of AI-driven radar sensing for remote health monitoring.
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