ArticleProceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference)2016
Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG.
Article in Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 34 citations in OpenAlex.
- Opioid misuse detection from cognitive and physiological data with temporal fusion deep learning.Drug and alcohol dependence · 2025Article
- Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensors.Drug and alcohol dependence · 2023Article
- Domain Adaptation Methods for Lab-to-Field Human Context Recognition.Sensors (Basel, Switzerland) · 2023Article
- OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2021Article
- Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- An Examination of the Feasibility of Detecting Cocaine Use Using Smartwatches.Frontiers in psychiatry · 2021Article
- Interpol review of controlled substances 2016-2019.Forensic science international. Synergy · 2020Review
- Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research.Addictive behaviors · 2018Review
- mHealth for the Detection and Intervention in Adolescent and Young Adult Substance Use Disorder.Current addiction reports · 2018Article
- rConverse: Moment by Moment Conversation Detection Using a Mobile Respiration Sensor.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2018Article
- Automatic Detection of Opioid Intake Using Wearable Biosensor.International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications · 2018Article
- Wearable ballistocardiogram and seismocardiogram systems for health and performance.Journal of applied physiology (Bethesda, Md. : 1985) · 2018Review
- Secondary Analysis to Advance Characterization of On-body Electrocardiographic Sensors in a Clinical Cocaine Self-administration Paradigm.Journal of addiction medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.