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Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning
Turke Althobaiti1, Stamos Katsigiannis2, Naeem Ramzan2
1Rafha Community College, Nothern Border University, Rafha 76413, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 10, 2020
Summary
This study uses wearable sensors and machine learning to detect falls in elderly individuals, achieving high accuracy. A new dataset, ShimFall&ADL, is released to advance fall detection research.
Area of Science:
- Biomedical Engineering
- Gerontology
- Machine Learning
Background:
- Falls are a major cause of injury and death in the elderly.
- Wearable sensors offer a non-intrusive solution for monitoring at-risk individuals.
Purpose of the Study:
- To develop and validate a machine learning model for detecting falls and activities of daily living (ADL) using wearable sensor data.
- To distinguish between fall events and normal ADLs.
Main Methods:
- Collected accelerometer data from 35 healthy individuals performing various ADLs and simulated falls.
- Extracted spatial and frequency domain features from the sensor data.
- Trained supervised machine learning models to classify fall versus non-fall events and ADLs.
Main Results:
- Achieved a 98.41% F1-score for distinguishing fall from non-fall events.
- Achieved an 88.11% F1-score for distinguishing between various ADLs, including falls.
- The "ShimFall&ADL" dataset was created and will be publicly released.
Conclusions:
- The proposed approach using wearable sensors and machine learning is highly effective for fall detection.
- The public release of the ShimFall&ADL dataset will support future research in this critical area.
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