A visuo-tactile deep network using a CNN autoencoder and ConvLSTM predicts four haptic attribute ratings from images and tool vibrations, beating single-modality baselines in leave-one-out tests.
Quantifying Haptic Affection of Car Door through Data-Driven Analysis of Force Profile
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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.
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Estimating Perceptual Attributes of Haptic Textures Using Visuo-Tactile Data
A visuo-tactile deep network using a CNN autoencoder and ConvLSTM predicts four haptic attribute ratings from images and tool vibrations, beating single-modality baselines in leave-one-out tests.