Quantifying Haptic Affection of Car Door through Data-Driven Analysis of Force Profile
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909673609756672 |
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| author | Awan, Mudassir Ibrahim Raza, Ahsan Hassan, Waseem Kyung, Ki-Uk Jeon, Seokhee |
| author_facet | Awan, Mudassir Ibrahim Raza, Ahsan Hassan, Waseem Kyung, Ki-Uk Jeon, Seokhee |
| contents | 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11382 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantifying Haptic Affection of Car Door through Data-Driven Analysis of Force Profile Awan, Mudassir Ibrahim Raza, Ahsan Hassan, Waseem Kyung, Ki-Uk Jeon, Seokhee Human-Computer Interaction 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. |
| title | Quantifying Haptic Affection of Car Door through Data-Driven Analysis of Force Profile |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.11382 |