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

Ecological Momentary Assessment (EMA) (309) mHealth for Data Collection and Research (955) Evaluation and Research Methodology for mHealth (208) Mobile Health (mhealth) (2939) Wearable Devices and Sensors (888) Participatory Epidemiology and Surveillance (119) Fitness Trackers and Smart Pedometers/Accelerometers (561) mHealth for Wellness, Behavior Change and Prevention (2871)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46347, first published .
Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

Authors of this article:

Zachary D King1 Author Orcid Image ;   Han Yu1 Author Orcid Image ;   Thomas Vaessen2, 3 Author Orcid Image ;   Inez Myin-Germeys2 Author Orcid Image ;   Akane Sano1 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Zachary D King   1 , MS ;   Han Yu   1 , MS ;   Thomas Vaessen   2, 3 , PhD ;   Inez Myin-Germeys   2 , PhD ;   Akane Sano   1 , PhD

1 Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States

2 Center For Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium

3 Department of Psychology, Health & Technology, University of Twente, Enschede, Netherlands

Corresponding Author:

  • Zachary D King, MS
  • Department of Electrical and Computer Engineering
  • Rice University
  • 6100 Main St
  • Houston, TX, 77005
  • United States
  • Phone: 1 713 348 0000
  • Email: zdk2@rice.edu