Emotion-Aware Interface Adaptation in Mobile Applications Based on Color Psychology and Multimodal User State Recognition
Bibliographic record
Abstract
Mobile applications that center on content discovery and lifestyle sharing increasingly involve emotionally influenced user behavior. This study examines how interface visuals can be adjusted in response to users’ emotional states, with a focus on visual tone adaptation guided by color psychology. A prototype system was developed that classifies emotional states using facial cues, voice characteristics, and interaction behavior, and then modifies the interface’s background colors, content framing and accent elements to reflect the detected affect. The system was evaluated through a controlled within-subject user study, in which 36 participants interacted with three interface versions reflecting distinct emotional tones: Happy, Sad, and Angry. Participants’ satisfaction, emotional alignment, and interaction behavior were measured during short usage sessions. Interfaces designed to match positive or low-arousal emotional states were generally associated with higher satisfaction scores and more sustained engagement. In contrast, interfaces that reflected high-arousal negative affect, while consistent with users’ moods, often led to shorter sessions and reduced interaction. The results indicate that emotionally tuned interface visuals can influence both perception and behavior during mobile interaction. Matching interface tone to user mood may improve comfort and alignment, but care is needed when responding to negative affect to avoid reinforcing disengagement. The findings contribute to ongoing work in interface design by showing how affect-sensitive styling, even when applied to basic visual properties, can support more emotionally coherent interaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".