TY - GEN
T1 - Personalized Modeling and Detection of Moments of Cannabis Use in Free-Living Environments
AU - Azghan, Reza Rahimi
AU - Glodosky, Nicholas C.
AU - Sah, Ramesh Kumar
AU - Cuttler, Carrie
AU - McLaughlin, Ryan
AU - Cleveland, Michael J.
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Cannabis Use Detection
KW - Machine Learning
KW - Stress
KW - Wearable
UR - http://www.scopus.com/inward/record.url?scp=85181584483&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85181584483&partnerID=8YFLogxK
U2 - 10.1109/BSN58485.2023.10331481
DO - 10.1109/BSN58485.2023.10331481
M3 - Conference contribution
AN - SCOPUS:85181584483
T3 - 2023 IEEE 19th International Conference on Body Sensor Networks, BSN 2023 - Proceedings
BT - 2023 IEEE 19th International Conference on Body Sensor Networks, BSN 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Body Sensor Networks, BSN 2023
Y2 - 9 October 2023 through 11 October 2023
ER -