Enhancing Product Emotional Experience Through Explicit-Implicit Interaction Design

Enhancing user emotional experience is crucial for improving product appeal and user satisfaction. However, current systems often struggle to integrate explicit and implicit interactions, particularly in capturing subtle emotional signals and dynamically adjusting feedback based on user behavior. To address these challenges, XFormerLite, a lightweight transformer-based model, is proposed to optimize emotional experiences via explicit–implicit signal integration, temporal modeling, and adaptive feedback. By combining explicit signals (e.g., voice and gestures) with implicit signals (e.g., facial expressions and posture), XFormerLite improves emotional engagement and personalizes real-time feedback. Experimental results indicate that XFormerLite achieves an emotion recognition accuracy of 86.2% on the Emo-DB dataset and 84.5% on IEMOCAP, along with significant improvements in response speed and emotion onset delay (EOD). This model presents a promising solution for optimizing emotional experiences in human–product interactions.