UMass Chan AffiliationsDepartment of Emergency Medicine, Division of Medical Toxicology
Document TypeConference Paper
Biomedical Devices and Instrumentation
Substance Abuse and Addiction
Translational Medical Research
MetadataShow full item record
AbstractA plethora of research shows that recreational drug overdoses result in major social and economic consequences. However, current illicit drug use detection in forensic toxicology is delayed and potentially compromised due to lengthy sample preparation and its subjective nature. With this in mind, scientists have been searching for ways to create a fast and easy method to detect recreational drug use. Therefore, we have developed a method for automatic detection of opioid intake using electrodermal activity (EDA), skin temperature and tri-axis acceleration data generated from a wrist worn biosensor. The proposed system can be used for home and hospital use. We performed supervised learning and extracted 23 features using time and frequency domain analysis to recognize pre- and post- opioid health conditions in patients. Feature selection procedures are used to reduce the number of features and processing time. For supervised learning, we compared three classifiers and selected the one with highest accuracy and sensitivity: decision tree, k-nearest neighbors (KNN) and eXtreme Gradient Boosting utilizing modified features. The results show that the proposed method can detect opioid use in real-time with 99% accuracy. Moreover, this method can be applied to identify other use of additional substances other than opioids. The numerical analysis is completed on data collected from 30 participants over a span of 4 months.
Mahmud MS, Fang H, Wang H, Carreiro S, Boyer E. Automatic Detection of Opioid Intake Using Wearable Biosensor. Int Conf Comput Netw Commun. 2018 Mar;2018:784-788. doi: 10.1109/ICCNC.2018.8390334. Epub 2018 Jun 21. PMID: 31853456; PMCID: PMC6919269. Link to article on publisher's site