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Cited by 116Emotion and Mood RecognitionContext-Aware Activity Recognition SystemsMental Health Research Topics

Objective assessment of depressive symptoms with machine learning and wearable sensors data

Ghandeharioun, Asma, Fedor, Szymon, Sangermano, Lisa, Ionescu, Dawn, Alpert, Jonathan, Dale, Chelsea, Sontag, David, Picard, Rosalind·2017·2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)
DOI 10.1109/acii.2017.8273620
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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.

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Crossref · 10.1109/acii.2017.8273620

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  • Other works have introduced methods for monitoring stroke in infants using wearable accelerometers and methods for assessing depressive symptoms utilizing wrist-worn sensors.

    “Other works have introduced methods for monitoring stroke in infants using wearable accelerometers [41] and methods for assessing depressive symptoms utilizing wrist-worn sensors [42].”

    From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [42]

  • The potential problem of overfitting was addressed by reducing the dimensionality of features using linear and non-linear transformations.

    “Similar to our previous work (37), we addressed the potential problem of overfitting by reducing the dimensionality of features using linear and non-linear transformations (see more details in A.2. in Supplementary Material).”

    From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [37]

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Authors
Ghandeharioun, A., Fedor, S., Sangermano, L., Ionescu, D., Alpert, J., Dale, C., Sontag, D., & Picard, R.
Affiliations
  • MIT Media Lab (US)
  • MIT Computer Science and Artificial Intelligence Laboratory (US)
ORCID
Venue
2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)
Publisher
IEEE
Published
2017-10
Pages
325-332
ISSN
2156-8103 (ISSN-L)
DOI
10.1109/acii.2017.8273620
OpenAlex
W2787556368
Cited by
116
References
28
Citations by year
2026: 7 · 2025: 8 · 2024: 16 · 2023: 18 · 2022: 22 · 2021: 18 · 2020: 11 · 2019: 7 · 2018: 9
FWCI
8.48
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Affective Computing and Intelligent Interaction and workshops (publishedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
depression, depressive symptoms, wearable sensors, electrodermal activity, Hamilton Depression Rating Scale, depression severity prediction, sleep behavior, motion activity, smartphone usage patterns, location patterns, missing data imputation, self-report measures

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