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Related Concept Videos

Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

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Synthetic and semisynthetic opioids are pivotal in pain management and tackling opioid addiction. Semisynthetic opioids, including morphinans (morphine derivatives), oxycodone, oxymorphone, hydrocodone, and hydromorphone, have improved pharmacokinetic profiles compared to morphine. Additionally, heroin and 6-MAM (6-Monoacetylmorphine) show better CNS penetration than morphine due to heightened lipid solubility. Hydromorphone, a potent opioid, undergoes hepatic metabolism to form the active...
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Opioids are a class of drugs that mimic endogenous opioid peptides and act on opioid receptors, and help in pain relief. These compounds are classified as natural, synthetic, or semi-synthetic. Natural opioids, like morphine, codeine, and thebaine, are derived from the opium poppy plant (Papaver somniferum or Papaver album) and are termed opiates. Synthetic opioids are artificial, while semi-synthetic opioids combine natural and synthetic compounds. Morphine, a prototypical opioid, possesses a...
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Updated: Jan 1, 2026

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
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Automatic Detection of Opioid Intake Using Wearable Biosensor.

Md Shaad Mahmud1, Hua Fang1, Honggang Wang2

  • 1Computer and Information Science and Department of Quantitative Health Science, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA.

International Conference on Computing, Networking, and Communications : [Proceedings]. International Conference on Computing, Networking and Communications
|December 20, 2019
PubMed
Summary

This study introduces a new, rapid method for detecting opioid use via a wrist-worn biosensor. The system accurately identifies opioid intake in real-time using physiological data, offering a faster alternative to traditional toxicology screening.

Keywords:
DrugOpioidReal-timeWearable

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Area of Science:

  • Biomedical Engineering
  • Toxicology
  • Data Science

Background:

  • Recreational drug overdoses cause significant societal and economic harm.
  • Current illicit drug use detection methods in forensic toxicology are slow and subjective.
  • There is a need for rapid, objective methods for detecting drug intake.

Purpose of the Study:

  • To develop an automated system for detecting opioid intake using biosensor data.
  • To enable real-time monitoring of opioid use in both home and clinical settings.
  • To provide a faster and more objective alternative to traditional drug testing.

Main Methods:

  • Utilized a wrist-worn biosensor to collect electrodermal activity (EDA), skin temperature, and tri-axis acceleration data.
  • Applied supervised learning with feature extraction from time and frequency domains.
  • Compared decision tree, k-nearest neighbors (KNN), and eXtreme Gradient Boosting classifiers, selecting the optimal one based on accuracy and sensitivity.

Main Results:

  • Achieved 99% accuracy in real-time detection of opioid use.
  • Successfully recognized pre- and post-opioid health conditions in patients.
  • Demonstrated the potential for identifying other substance use beyond opioids.

Conclusions:

  • The developed biosensor system offers a highly accurate and real-time solution for detecting opioid intake.
  • This technology has broad applicability for monitoring substance use and could be extended to other drugs.
  • The method provides a significant advancement over conventional forensic toxicology techniques.