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.
Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing
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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.