Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study.

Journal: JMIR mHealth and uHealth
PMID: 32990638

Abstract

BACKGROUND: Emotional state in everyday life is an essential indicator of health and well-being. However, daily assessment of emotional states largely depends on active self-reports, which are often inconvenient and prone to incomplete information. Automated detection of emotional states and transitions on a daily basis could be an effective solution to this problem. However, the relationship between emotional transitions and everyday context remains to be unexplored.

Authors

  • Madeena Sultana
    Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
  • Majed Al-Jefri
    Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
  • Joon Lee
    Health Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada.

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