Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile
Year: 2025
Type: article
Abstract: Haptic affection plays a crucial role in user experience, particularly in the automotive industry where the tactile quality of components can influence customer satisfaction. This study aims to accurately predict the affective property of a car door by only watching the force or torque profile of it when opening. To this end, a deep learning model is designed to capture the underlying relationships between force profiles and user-defined adjective ratings, providing insights into the door-opening experience. The dataset employed in this research includes force profiles and user adjective ratings collected from six distinct car models, reflecting a diverse set of door-opening characteristics and tactile feedback. The model’s performance is assessed using Leave-One-Out Cross-Validation, a method that measures its generalization capability on unseen data. The results demonstrate that the proposed model achieves a high level of prediction accuracy, indicating its potential in various applications related to haptic affection and design optimization in the automotive industry.
Source: IEEE Access
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Funders: University of Engineering and Technology, Lahore, Kyung Hee University, Electronics and Telecommunications Research Institute, Korea Advanced Institute of Science and Technology, Iran Telecommunication Research Center, National Research Council of Science and Technology, National University of Science and Technology, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
OpenAlex ID: W4411949001