Honorable Mention
We present a visuo-haptic crossmodal model of shape perception designed for shape-changing handheld controllers. The model uses the inertia tensor of an object to bridge the two senses. The model was constructed from the results of three perceptual experiments. In the first two experiments, we validate that the primary moment and product of inertia (MOI and POI) in the inertia tensor have critical effects on the haptic perception of object length and asymmetry. Then, we estimate a haptic-to-visual shape matching model using MOI and POI as two link variables from the results of the third experiment for crossmodal magnitude production. Finally, we validate in a summative user study that the inverse of the shape matching model is effective for pairing a perceptually-congruent haptic object from a virtual object-the functionality we need for shape-changing handheld interfaces to afford perceptually-fulfilling sensory experiences in virtual reality.
https://doi.org/10.1145/3544548.3580724
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)