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A Kalman filter model for predicting discrimination performance in free and restricted haptic explorations.

Metzger, A. and Drewing, K., 2021. A Kalman filter model for predicting discrimination performance in free and restricted haptic explorations. In: IEEE World Haptics Conference (WHC), 2021, 6-9 July 2021, Montreal, Canada, 439-444.

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Official URL: https://ieeexplore.ieee.org/document/9517242

DOI: 10.1109/WHC49131.2021.9517242

Abstract

Haptic exploration of objects usually consists of repeated exploratory movements and our perception of their properties is the result of the integration of information gained during each of these single movements. The serial nature of information integration in haptic perception requires that sensory estimates from single exploratory movements are retained in memory. Here we propose an optimal model for serial integration of information in haptic explorations which considers memory limitations. We tested the model by predicting discrimination performance in free and restricted (fixed number of indentations and varied number of switches between the stimuli) explorations of softness. Our model overall well predicts performance given different exploratory patterns in both free and restricted explorations. The model slightly overestimates performance in the restricted exploration and predictions are accurate in free explorations. These results suggest that integration of information can be well approximated by our model, in particular in free haptic exploration. We further tested whether participants prefer explorations which maximize performance. The model predicts that with constant number of indentations switching between the stimuli increases performance. Our results show that participants increase the number of switches only up to three switches, suggesting a trade-off between muscular switching costs and performance.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Conferences , Memory management; Switches; Predictive models; Haptic interfaces; Kalman filters
Group:Faculty of Science & Technology
ID Code:37183
Deposited By: Symplectic RT2
Deposited On:18 Jul 2022 11:19
Last Modified:18 Jul 2022 11:19

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