Personalized Modeling and Detection of Moments of Cannabis Use in Free-Living Environments

Abstract

Coping with stress is reportedly one of the main reasons for chronic cannabis use. Developing a real-time system that offers cannabis users alternative methods to cope with stress is of interest in medical applications. To develop such a system, it is necessary to design a reliable mechanism for identifying cannabis use sessions in uncontrolled environments using physiological markers captured with wearable sensors. Therefore, the primary objective of this study is to design a system that can identify sessions of cannabis consumption by utilizing one of the most significant biomarkers of stress, Electrodermal Activity (EDA). We conducted a user study to collect physiological sensor data in real-life setting. We then model the cannabis use detection as a supervised learning problem and train a neural network model. To improve the performance of the proposed model for a specific subject, transfer learning techniques were used to retrain the base model on the new user data. Trained model achieved average f1-score of 0.68 and accuracy of 71.58% on the test data from Leave One Subject Out (LOSO) analysis. After applying transfer learning, the retrained model achieved average f1-score of 0.8 and accuracy of 83.61% when detecting the cannabis consumption period for the same subjects

Reza Rahimi Azghan

Grad Research Associate

I am a Ph.D. student at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.

Hassan Ghasemzadeh

Director

Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).