Quantifying Haptic Affection of Car Door through Data-Driven Analysis of Force Profile

2024-11-18

arXiv.org

AI Summary

Quantitative evaluation of door-opening haptic quality—such as perceived “heaviness” or “smoothness”—remains challenging due to its subjective, multimodal nature. Method: This study proposes a novel end-to-end deep learning framework that directly maps raw force/torque time-series signals to multidimensional subjective adjective ratings. The model integrates LSTM and Temporal Convolutional Networks (TCN) for temporal modeling, adaptive signal preprocessing, and cross-vehicle data fusion, validated via leave-one-out cross-validation to ensure generalizability. Contribution/Results: Evaluated on real-world door-opening data from six vehicle models, the method achieves an average R² > 0.89—significantly outperforming conventional statistical models. Crucially, it enables objective, sensor-free quantification of subjective haptic perception, eliminating the need for additional instrumentation. This work establishes a new paradigm for quantifying and engineering automotive human–machine interaction, bridging perceptual experience with objective design metrics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Predict car door affective property using force profiles
• Relate force profiles to user adjective ratings via deep learning
• Assess model performance with Leave-One-Out Cross-Validation

Innovation

• Deep learning model predicts haptic affection
• Force profiles linked to user adjective ratings
• Leave-One-Out Cross-Validation ensures generalization