Hybrid Kansei-Visual Interaction Framework for Human-Centered Product Form Design Based on SHAP-Kano and Transformer-BiLSTM

In the experience economy, user attention in product interaction has shifted from functional performance toward emotional and perceptual experience, making Kansei factors increasingly important in product form design (PFD). However, the identification and prioritization of customer requirements (CRs) often rely on subjective judgment, and form design decisions remain constrained by designers’ intuition. This study proposes a hybrid human-centered Kansei Engineering (KE) framework that integrates explainable machine learning with visual interaction analysis. Online user reviews are utilized to objectively extract and evaluate affective requirements, while visual sequence features derived from eye-tracking experiments capture users’ perceptual and attentional responses to product forms. Key CRs are identified using Latent Dirichlet Allocation, and sentiment information is analyzed through BERT and SnowNLP. An ‌eXtreme Gradient Boosting‌ (XGBoost) model combined with Shapley Additive Explanations (SHAP) is employed to perform Kano-based requirement classification and importance weighting. Subsequently, a Transformer-BiLSTM model maps Kansei factors to form features based on users’ visual sequence data, enabling the prediction of form attribute combinations aligned with user affective expectations. A Bluetooth speaker design case study demonstrates the applicability of the proposed framework. Results indicate that integrating affective requirement modeling with visual attention analysis effectively supports data-driven and human-centered product form design, contributing to research on visual perception and interaction in human-computer interaction contexts.