1. Emotional Expression and Mental Health: Decoding Color and Drawing Styles with Python and OpenCV
- Author
-
Hui-Ching Weng, Tanida Julvanichpong, Patchana Jaidee, Kanchana Piboon, Puangtong Inchai, Longchar Imcha, Liang-Yun Huang, and Pi-Chun Huang
- Subjects
color ,drawing styles ,emotional expression ,mental health ,Public aspects of medicine ,RA1-1270 ,Social Sciences - Abstract
Introduction: Despite advancements in understanding color-emotion correlations, the influence of mental health on this relationship is less studied. Our research explores how mental health impacts emotional expression through color and depiction style. Methods: Engaging 212 students, we collected 1272 digital drawings representing six primary emotions: anger, fear, sadness, calm, excitement, and happiness. Our study, conducted from November to December 2023, utilized a cross-sectional design. Participants were recruited through convenience sampling. We collected both survey responses and participant-generated images. Using Python and OpenCV, we quantified subjective emotional expressions. Results: Participants predominantly chose red for anger (57.43%), illustrating the red usage percentage for anger, black for fear (38.14%), gray and blue for sadness (27.86%, 27.83%), green for calm (25.73%), and red for both excitement (27.26%) and happiness (22.85%). Fear was the most frequent color fill at 31.58%, with anger the least at 24.95%. Tangible imagery was prevalent (88%–96.2%), while abstract styles were most common in fear depictions (12%). Emotion significantly influences color choices (P = 0.017~
- Published
- 2024
- Full Text
- View/download PDF