Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile
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...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11062851/ |
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| author | Mudassir Ibrahim Awan Ahsan Raza Waseem Hassan Ki-Uk Kyung Seokhee Jeon |
| author_facet | Mudassir Ibrahim Awan Ahsan Raza Waseem Hassan Ki-Uk Kyung Seokhee Jeon |
| author_sort | Mudassir Ibrahim Awan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7fdcbd63ecec4e50a749c77e78433b2e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7fdcbd63ecec4e50a749c77e78433b2e2025-08-20T03:50:59ZengIEEEIEEE Access2169-35362025-01-011311972611973910.1109/ACCESS.2025.358506711062851Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force ProfileMudassir Ibrahim Awan0https://orcid.org/0009-0005-1514-6306Ahsan Raza1https://orcid.org/0000-0003-4064-7098Waseem Hassan2https://orcid.org/0000-0003-3922-5648Ki-Uk Kyung3https://orcid.org/0000-0002-2707-8516Seokhee Jeon4https://orcid.org/0000-0002-0413-9646Department of Computer Engineering, Kyung Hee University, Yongin, Gyeonggi, South KoreaDepartment of Computer Engineering, Kyung Hee University, Yongin, Gyeonggi, South KoreaDepartment of Computer Engineering, Kyung Hee University, Yongin, Gyeonggi, South KoreaDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South KoreaDepartment of Metaverse, Kyung Hee University, Yongin, Gyeonggi, South KoreaHaptic 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.https://ieeexplore.ieee.org/document/11062851/Car door torque profileuser experiencehaptic feedbackhuman haptic perceptiondeep learning |
| spellingShingle | Mudassir Ibrahim Awan Ahsan Raza Waseem Hassan Ki-Uk Kyung Seokhee Jeon Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile IEEE Access Car door torque profile user experience haptic feedback human haptic perception deep learning |
| title | Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile |
| title_full | Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile |
| title_fullStr | Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile |
| title_full_unstemmed | Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile |
| title_short | Quantifying Haptic Affection of Car Door Through Data-Driven Analysis of Force Profile |
| title_sort | quantifying haptic affection of car door through data driven analysis of force profile |
| topic | Car door torque profile user experience haptic feedback human haptic perception deep learning |
| url | https://ieeexplore.ieee.org/document/11062851/ |
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