Machine learning models using smartwatch data from a 54-participant test-track study detect alcohol-impaired driving with participant-averaged AUROC of 0.88 for any intoxication and 0.86 above 0.05 g/dL.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A dual-branch system using frequency edge cues and CLIP-based synthetic patch detection for accurate, resolution-independent image forgery localization.
citing papers explorer
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Detecting Drunk Driving Using Off-the-Shelf Smartwatches
Machine learning models using smartwatch data from a 54-participant test-track study detect alcohol-impaired driving with participant-averaged AUROC of 0.88 for any intoxication and 0.86 above 0.05 g/dL.
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EDGER: EDge-Guided with HEatmap Refinement for Generalizable Image Forgery Localization
A dual-branch system using frequency edge cues and CLIP-based synthetic patch detection for accurate, resolution-independent image forgery localization.