Extensive research has been done in haptic feedback for texture simulation in virtual reality (VR). However, it is challenging to modify the perceived tactile texture of existing physical objects which usually serve as anchors for virtual objects in mixed reality (MR). In this paper, we present ViboPneumo, a finger-worn haptic device that uses vibratory-pneumatic feedback to modulate (i.e., increase and decrease) the perceived roughness of the material surface contacted by the user's fingerpad while supporting the perceived sensation of other haptic properties (e.g., temperature or stickiness) in MR. Our device includes a silicone-based pneumatic actuator that can lift the user's fingerpad on the physical surface to reduce the contact area for roughness decreasing, and an on-finger vibrator for roughness increasing. Our user-perception experimental results showed that the participants could perceive changes in roughness, both increasing and decreasing, compared to the original material surface. We also observed the overlapping roughness ratings among certain haptic stimuli (i.e., vibrotactile and pneumatic) and the originally perceived roughness of some materials without any haptic feedback. This suggests the potential to alter the perceived texture of one type of material to another in terms of roughness (e.g., modifying the perceived texture of ceramics as glass). Lastly, a user study of MR experience showed that ViboPneumo could significantly improve the MR user experience, particularly for visual-haptic matching, compared to the condition of a bare finger. We also demonstrated a few application scenarios for ViboPneumo.
Supporting eyes-free interaction, mobility and encumbrance, while providing a broad set of commands on a smartwatch display is a difficult, yet important, task. Bezel-to-bezel (B2B) gestures are valuable for rapid command invocation during eyes-free operation, however we lack knowledge regarding B2B interactions on circular devices during common usage scenarios. We aim to improve our understanding of the dynamics of B2B interactions in these scenarios by conducting two studies and a third analysis: First, we explore the performance of B2B in a seated position; second, we explore the effect of mobility and encumbrance on the B2B interaction; finally, we improve on the B2B accuracies by calculating features and utilizing machine learning. With the limited interaction capabilities on smartwatches and the importance of the scenario of use, we conclude with applications and design guidelines for improved utilization of B2B that enables effective smartwatch control while in common, mobile and eyes-free scenarios.
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