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2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII) (2017)
San Antonio, TX, USA
Oct. 23, 2017 to Oct. 26, 2017
ISSN: 2156-8111
ISBN: 978-1-5386-0564-6
pp: 325-332
Asma Ghandeharioun , Media Lab, MIT, Cambridge, MA 02139
Szymon Fedor , Media Lab, MIT, Cambridge, MA 02139
Lisa Sangermano , DCRP, MGH, Boston, MA 02114
Dawn Ionescu , DCRP, MGH, Boston, MA 02114
Jonathan Alpert , DCRP, MGH, Boston, MA 02114
Chelsea Dale , DCRP, MGH, Boston, MA 02114
David Sontag , CSAIL, MIT, Cambridge, MA 02139
Rosalind Picard , Media Lab, MIT, Cambridge, MA 02139
ABSTRACT
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.
INDEX TERMS
Mood, Wearable sensors, Temperature measurement, Biomarkers, Mobile handsets, Skin
CITATION

A. Ghandeharioun et al., "Objective assessment of depressive symptoms with machine learning and wearable sensors data," 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, TX, USA, 2017, pp. 325-332.
doi:10.1109/ACII.2017.8273620
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