Advances in Haptics 2010
DOI: 10.5772/8712
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Sensory Properties in Fusion of Visual/Haptic Stimuli Using Mixed Reality

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Cited by 15 publications
(6 citation statements)
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References 21 publications
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“…Our work builds upon previous work in combining visual and haptic impressions to influence material perception (Kitahara et al, 2010;Degraen et al, 2019), and fabrication technologies for designing varying psychophysical impressions (Piovarči et al, 2016;Groeger et al, 2019;Degraen et al, 2021b). From our results, we see that haptically-varying metamaterial surfaces enable material perception in combination with visual textures.…”
Section: Metamaterials For Passive Haptic Feedbacksupporting
confidence: 72%
“…Our work builds upon previous work in combining visual and haptic impressions to influence material perception (Kitahara et al, 2010;Degraen et al, 2019), and fabrication technologies for designing varying psychophysical impressions (Piovarči et al, 2016;Groeger et al, 2019;Degraen et al, 2021b). From our results, we see that haptically-varying metamaterial surfaces enable material perception in combination with visual textures.…”
Section: Metamaterials For Passive Haptic Feedbacksupporting
confidence: 72%
“…There are some studies about the influence of MR visual stimulation on haptic perception, including our previous studies [4][5][6][7][8]. Kitahara et al confirmed that visual stimulation in an MR environment affects the haptic perception of texture and sharpness of an edge [18]; this idea is similar to [4]. Nakahara et al described the result of experiments on the curvature factor of edges [19].…”
Section: Related Workmentioning
confidence: 76%
“…While in Experiment 2 we presented the feedback on their forearm, in preliminary studies, we found that users found identification difficult even when the feedback was provided near their shoulder, thigh or calf,or even the finger of their other hand (though performance in this case was improved a bit). This is maybe due to the fact that visual feedback is a crucial contributor for the identification of materials [25], [26]. However, in Fig.…”
Section: Lessons For Direct-texture-feedbackmentioning
confidence: 93%