This paper is in the following e-collection/theme issue:

Digital Biomarkers and Digital Phenotyping (191) mHealth for Wellness, Behavior Change and Prevention (2872) Machine Learning (1465) mHealth for Data Collection and Research (955) Mobile Health in Psychiatry (427) Methods and New Tools in Mental Health Research (454) Diagnostic Tools in Mental Health (242) Depression and Mood Disorders; Suicide Prevention (1363)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24465, first published .
Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach

Authors of this article:

Emese Sükei1 Author Orcid Image ;   Agnes Norbury2 Author Orcid Image ;   M Mercedes Perez-Rodriguez2 Author Orcid Image ;   Pablo M Olmos1, 3 Author Orcid Image ;   Antonio Artés1, 3 Author Orcid Image
Article Authors Cited by (19) Tweetations (5) Metrics

Emese Sükei   1 , MSc ;   Agnes Norbury   2 , PhD ;   M Mercedes Perez-Rodriguez   2 , MD, PhD ;   Pablo M Olmos   1, 3 , PhD ;   Antonio Artés   1, 3 , PhD

1 Signal Theory and Communications Department, Universidad Carlos III de Madrid, Leganés, Spain

2 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States

3 Gregorio Marañón Health Research Institute, Madrid, Spain

Corresponding Author:

  • Emese Sükei, MSc
  • Signal Theory and Communications Department
  • Universidad Carlos III de Madrid
  • Torres Quevedo Bldg
  • Av de la Universidad, 30
  • Leganés, 28911
  • Spain
  • Phone: 34 916248839
  • Email: esukei@tsc.uc3m.es