TY - GEN
T1 - Objective assessment of depressive symptoms with machine learning and wearable sensors data
AU - Ghandeharioun, Asma
AU - Fedor, Szymon
AU - Sangermano, Lisa
AU - Ionescu, Dawn
AU - Alpert, Jonathan
AU - Dale, Chelsea
AU - Sontag, David
AU - Picard, Rosalind
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Depression is the major cause of years lived in disability world-wide; however, its diagnosis and tracking methods still rely mainly on assessing self-reported depressive symptoms, methods that originated more than fifty years ago. These methods, which usually involve filling out surveys or engaging in face-to-face interviews, provide limited accuracy and reliability and are costly to track and scale. In this paper, we develop and test the efficacy of machine learning techniques applied to objective data captured passively and continuously from E4 wearable wristbands and from sensors in an Android phone for predicting the Hamilton Depression Rating Scale (HDRS). Input data include electrodermal activity (EDA), sleep behavior, motion, phone-based communication, location changes, and phone usage patterns. We introduce our feature generation and transformation process, imputing missing clinical scores from self-reported measures, and predicting depression severity from continuous sensor measurements. While HDRS ranges between 0 and 52, we were able to impute it with 2.8 RMSE and predict it with 4.5 RMSE which are low relative errors. Analyzing the features and their relation to depressive symptoms, we found that poor mental health was accompanied by more irregular sleep, less motion, fewer incoming messages, less variability in location patterns, and higher asymmetry of EDA between the right and the left wrists.
AB - Depression is the major cause of years lived in disability world-wide; however, its diagnosis and tracking methods still rely mainly on assessing self-reported depressive symptoms, methods that originated more than fifty years ago. These methods, which usually involve filling out surveys or engaging in face-to-face interviews, provide limited accuracy and reliability and are costly to track and scale. In this paper, we develop and test the efficacy of machine learning techniques applied to objective data captured passively and continuously from E4 wearable wristbands and from sensors in an Android phone for predicting the Hamilton Depression Rating Scale (HDRS). Input data include electrodermal activity (EDA), sleep behavior, motion, phone-based communication, location changes, and phone usage patterns. We introduce our feature generation and transformation process, imputing missing clinical scores from self-reported measures, and predicting depression severity from continuous sensor measurements. While HDRS ranges between 0 and 52, we were able to impute it with 2.8 RMSE and predict it with 4.5 RMSE which are low relative errors. Analyzing the features and their relation to depressive symptoms, we found that poor mental health was accompanied by more irregular sleep, less motion, fewer incoming messages, less variability in location patterns, and higher asymmetry of EDA between the right and the left wrists.
UR - http://www.scopus.com/inward/record.url?scp=85047362862&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047362862&partnerID=8YFLogxK
U2 - 10.1109/ACII.2017.8273620
DO - 10.1109/ACII.2017.8273620
M3 - Conference contribution
AN - SCOPUS:85047362862
T3 - 2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
SP - 325
EP - 332
BT - 2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
Y2 - 23 October 2017 through 26 October 2017
ER -