Effects of active or passive touch on perception precision of edge direction using tactile mouse exhibiting convex dot-patterns on the palm

Komura, Hiraku; Ohka, Masahiro

doi:10.1109/iris.2017.8250103

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Currently, the perception of the tactile properties of objects depends mainly on the display of hardware. Komura et al studied a tactile mouse that converts the design parameters of a virtual reality device into corresponding bump patterns for the palm of the user [4]. The principle is to connect the mouse to the dot matrix convex plate and move the mouse cursor on the image.…”

Section: Related Workmentioning

confidence: 99%

Learning cross‐modal visual‐tactile representation using ensembled generative adversarial networks

Liu

Zhou

et al. 2019

Cognitive Computation and Systems

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In this study, the authors study a deep learning model that can convert vision into tactile information, so that different texture images can be fed back to the tactile signal close to the real tactile sensation after training and learning. This study focuses on the classification of different image visual information and its corresponding tactile feedback output mode. A training model of ensembled generative adversarial networks is proposed, which has the characteristics of simple training and stable efficiency of the result. At the same time, compared with the previous methods of judging the tactile output, in addition to subjective human perception, this study also provides an objective and quantitative evaluation system to verify the performance of the model. The experimental results show that the learning model can transform the visual information of the image into the tactile information, which is close to the real tactile sensation, and also verify the scientificity of the tactile evaluation method.

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Section: Related Workmentioning

confidence: 99%