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.
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh–A Python package)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.