Authors:
Ya Wang
1
;
Peiman Sarvari
2
and
Djamel Khadraoui
2
Affiliations:
1
Faculty of Science, Technology and Medicine, University of Luxembourg, Esch sur Alzette, Luxembourg
;
2
IT for Innovative Services, Luxembourg University of Science and Technology, Esch sur Alzette, Luxembourg
Keyword(s):
Wearable Fall Detection, Feature Extraction, Threshold, Machine Learning, Inertial Sensors.
Abstract:
In the healthcare sector, specifically for elderly care, accurate and efficient fall detection is crucial. We present an advanced fall detection methodology tailored for wearable systems. Our approach blends threshold-based screening with machine learning models like Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost. Utilizing 65 features extracted from the gyroscope and accelerometer data from Inertial Measurement Units, our method addresses the class imbalance often found between Activities of Daily Living and actual fall events. Threshold-based pre-screening serves to mitigate the class imbalance of the fall dataset, making the subsequent machine-learning classification more effective. Validation on two open-source IMU datasets, Sisfall and FallAllD, achieving high accuracy rates of 99.55%, 99.68% (wrist), 99.76% (waist), and 99.52% (neck), shows our model surpassing existing solutions in detection accuracy. Furthermore, our strategic feature e
xtraction not only enhances the model’s performance but also allows for a fourfold reduction by using the 15 most important features in data transmission without sacrificing accuracy. These findings underscore the efficiency and potential of our methodology, indicating that wearables can indeed be powerful tools for high-precision fall detection with minimal data overhead.
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