A window-based F1 metric that rewards detections within a time tolerance reveals statistically significant stress-event prediction where pointwise F1 scores are all zero.
ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting
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abstract
Stress detection and classification from wearable sensor data is an emerging area of research with significant implications for individuals' physical and mental health. In this work, we introduce a new dataset, ADARP, which contains physiological data and self-report outcomes collected in real-world ambulatory settings involving individuals diagnosed with alcohol use disorders. We describe the user study, present details of the dataset, establish the significant correlation between physiological data and self-reported outcomes, demonstrate stress classification, and make our dataset public to facilitate research.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric
A window-based F1 metric that rewards detections within a time tolerance reveals statistically significant stress-event prediction where pointwise F1 scores are all zero.