OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks

UMMS Affiliation

Division of Medical Toxicology, Department of Emergency Medicine; School of Medicine

Publication Date


Document Type

Conference Proceeding


Biomedical Devices and Instrumentation | Emergency Medicine | Substance Abuse and Addiction


Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours ( approximately 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R (2) coefficient of 0.85.


Channel and Temporal Attention, Depthwise convolutions, Opioid administration, Physiological signal, Temporal convolutional network

DOI of Published Version



Gullapalli BT, Carreiro S, Chapman BP, Ganesan D, Sjoquist J, Rahman T. OpiTrack: A Wearable-based Clinical Opioid Use Tracker with Temporal Convolutional Attention Networks. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2021 Sep;5(3):102. doi: 10.1145/3478107. Epub 2021 Sep 14. PMID: 35291374; PMCID: PMC8920039. Link to article on publisher's site

Journal/Book/Conference Title

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies

Related Resources

Link to Article in PubMed

PubMed ID