TY - JOUR
T1 - Emotional Expression and Mental Health
T2 - Decoding Color and Drawing Styles with Python and OpenCV
AU - Weng, Hui Ching
AU - Julvanichpong, Tanida
AU - Jaidee, Patchana
AU - Piboon, Kanchana
AU - Inchai, Puangtong
AU - Imcha, Longchar
AU - Huang, Liang Yun
AU - Huang, Pi Chun
N1 - Publisher Copyright:
© 2024 Asian Journal of Social Health and Behavior.
PY - 2024
Y1 - 2024
N2 - 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~<0.001), color number (P < 0.001), and image coverage (P = 0.003). Drawing style comparisons across three mental health levels showed minimal yet significant usage differences: black for fear (P = 0.037), color variability (P = 0.027), and purple for calm (P = 0.012). Despite these differences, mental health did not significantly moderate the relationships between color use and drawing styles. Conclusion: Our study advanced color-emotion research by letting participants select colors, highlighting minimal mental health impacts on emotional expression and consistent associations across cultures and ages. Using Python and OpenCV to quantify qualitative images has greatly increased analysis objectivity, substantially progressing the field.
AB - 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~<0.001), color number (P < 0.001), and image coverage (P = 0.003). Drawing style comparisons across three mental health levels showed minimal yet significant usage differences: black for fear (P = 0.037), color variability (P = 0.027), and purple for calm (P = 0.012). Despite these differences, mental health did not significantly moderate the relationships between color use and drawing styles. Conclusion: Our study advanced color-emotion research by letting participants select colors, highlighting minimal mental health impacts on emotional expression and consistent associations across cultures and ages. Using Python and OpenCV to quantify qualitative images has greatly increased analysis objectivity, substantially progressing the field.
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U2 - 10.4103/shb.shb_138_24
DO - 10.4103/shb.shb_138_24
M3 - Article
AN - SCOPUS:85202918087
SN - 2772-4204
VL - 7
SP - 116
EP - 122
JO - Asian Journal of Social Health and Behavior
JF - Asian Journal of Social Health and Behavior
IS - 3
ER -
