Stiffness Discrimination Threshold Prediction for Haptic Exploration using Deep Learning

This study explores the use of deep learning to predict stiffness discrimination thresholds using psychophysical experimental data. Haptic technology, integral to various fields, enables realistic simulations and interactions with virtual worlds. Traditional methods for assessing tactile sensitivity such as ability to discriminate stiffness differences are laborintensive, requiring numerous trials. This research investigates deep learning as a solution to reduce experimental demands while maintaining reliable assessments. Thirteen participants engaged in experiments using a haptic device to measure their stiffness discrimination thresholds. Data on palpation force, fingertip velocity, and Weber fractions were collected and analyzed. The study employed binary and multiclass classification, as well as regression, to predict Weber fractions using deep learning models. A comparison of the results indicated that deep learning models outperformed traditional machine learning algorithms for the same prediction task, demonstrating higher accuracy. The findings suggest that deep learning can effectively streamline the assessment of stiffness discrimination during haptic exploration, offering a promising tool for future research and applications in exploration of virtual worlds using haptics.