The paper proposes FEDT, an ensemble of decision trees for fall detection, integrated into a mobile-cloud system that filters ADLs on-device and achieves 1-3% higher sensitivity and specificity than compared methods.
Window-size impact on detection rate of wearable-sensor-based fall detection using supervised machine learning,
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A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning
The paper proposes FEDT, an ensemble of decision trees for fall detection, integrated into a mobile-cloud system that filters ADLs on-device and achieves 1-3% higher sensitivity and specificity than compared methods.