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.

Publisher's description: This book provides a candid assessment and practical knowledge about the current technological advancements of the wireless healthcare system. This book presents the competencies of modeling e-health framework, medical wireless body sensor networks, communication technologies for mobile health, nanotechnology innovations in medicine, security issues for medical records, personalized services in healthcare applications, and Big Data for wireless health. This book covers multiple research perspectives in order to address the strong need for interdisciplinary research in the area of wireless health, such as the interactive research among biomedical sensor technology, intelligent textiles and advanced wireless network technology. The interactions involve experts from multidisciplinary fields including medical, information technology and computing fields. Designed as a study tool for graduate students, researchers, and medical professionals, this book is also valuable for business managers, entrepreneurs, and investors within the medical and healthcare industries. It is useful for anyone who cares about the future opportunities in healthcare systems.