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.
D e t e c t i n g u n s e e n f a l l s f r o m wearable devices using channel-wise ensemble of autoencoders
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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.