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Monitoring Opioid-Use-Disorder Treatment Adherence Using Smartwatch Gesture Recognition.

Andrew Smith¹, Kuba Jerzmanowski¹, Phyllis Raynor²

¹Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

Smartwatches can identify medication-taking gestures for opioid use disorder (OUD) treatment using machine learning. This technology shows promise for improving medication adherence and patient monitoring in OUD management.

Keywords:

context-aware environments ecological momentary assessment human activity recognition machine learning medication detection neural networks smart healthcare wearable sensors

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Area of Science:

Digital Health
Machine Learning in Healthcare
Substance Use Disorder Treatment

Background:

The opioid epidemic significantly affects pregnant women with opioid use disorder (OUD).
Effective monitoring of medication adherence is crucial for OUD treatment success.
Current monitoring methods for medication-taking can be limited.

Purpose of the Study:

To explore the feasibility of using machine learning algorithms with consumer-grade smartwatches.
To identify medication-taking gestures for opioid use disorder (OUD) treatments, specifically methadone and buprenorphine.
To assess the potential for real-time monitoring and improved medication adherence.

Main Methods:

Utilized Ticwatch E and E3 smartwatches with custom ASPIRE software to collect gesture data.
Recruited 16 female university students who simulated medication-taking gestures in a controlled lab setting.
Employed a RegNet-style 1D ResNet model for analyzing smartwatch-collected gesture data.

Main Results:

The machine learning model achieved high performance in classifying medication-taking gestures.
Achieved F1 scores of 0.89 (medication types), 0.88 (medication vs. daily activities), and 0.96 (any medication gesture).
Demonstrated accuracy in distinguishing between different medication-taking actions and daily activities.

Conclusions:

Smartwatch-based gesture recognition is a feasible approach for monitoring OUD medication adherence.
This technology has the potential to enhance real-time patient monitoring and improve treatment outcomes.
Further real-world validation is needed due to simulated gestures and a small participant pool.