Paper Info
Title
Automatic Detection of Opioid Intake Using Wearable Biosensor
Abstract
A 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.
Year
DOI
Venue
2018
10.1109/ICCNC.2018.8390334
2018 International Conference on Computing, Networking and Communications (ICNC)
Keywords
DocType
Volume
Opioid,Drug,Real-time,Wearable
Conference
2018
ISSN
ISBN
Citations 
2325-2626
978-1-5386-3653-4
2
PageRank 
References 
Authors
0.66
5
5
Name
Order
Citations
PageRank
Shaad Mahmud1142.62
Hua Fang234332.48
Honggang Wang31365124.06
Stephanie Carreiro441.15
Edward W. Boyer520.66