Pith. sign in

REVIEW 1 cited by

ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2206.14568 v1 pith:U44GRKQU submitted 2022-06-14 cs.HC eess.SP

Ramesh Kumar Sah , Michael McDonell , Patricia Pendry , Sara Parent , Hassan Ghasemzadeh , Michael J Cleveland This is my paper · ORCID
classification cs.HCeess.SP
keywords datasetdatastressadarpalcoholclassificationindividualsoutcomes
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric

    cs.LG 2025-09 conditional novelty 4.0 of 10

    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.

Pith tools