Ultrasound Rendering of Tactile Interaction with Fluids

Abstract

When we interact with fluid media, e.g., with our hands, we experience a spatially and temporally varying pressure field on our skin, which depends on the density and viscosity of the fluid, as well as the relative motion between our hands and the surrounding flow. Ultrasound phased arrays stimulate skin in mid air by controlling pressure waves at particular spatial locations. In this work, we explore the connection between the pressure-based stimulation of ultrasound haptics and the actual pressure field experienced when interacting with fluid media, to devise a novel algorithm for ultrasoundbased rendering of tactile interaction with fluids. Our algorithm extracts the target pressure field on a virtual hand from an interactive fluid simulation, and formulates the computation of the rendered pressure as an optimization problem. We have designed an efficient solver for this optimization problem, and we show results of interactive experiments with several fluid simulations.

Natural Tactile Interaction with Virtual Clay

“…Barreiro et al formulated optimization-based solutions to this rendering problem for both amplitude and spatiotemporal modulation. Their solution for amplitude modulation formulates and solves rendering as a clustering problem [22]. Their solution for spatiotemporal modulation, on the other hand, formulates and solves rendering as a path routing problem [23].…”

“…Our ultrasound rendering algorithm follows the clusteringbased amplitude-modulation approach of Barreiro et al [22], with two notable differences. First, the target pressure field is extracted from a PBD simulation of clay.…”

“…Similar to previous works on ultrasound rendering of fluids, we extract a pressure field from the interaction between the simulated hand and the clay material. However, since our PBF simulation model differs from the Eulerian model of Barreiro et al [22] or the SPH model of Jang and Park [24], we require a different method to compute the pressure field on the simulated hand.…”

“…4. We follow the clustering algorithm of Barreiro et al [22], augmented with the perceptual weighting function. Specifically, we set weights α = 1 at finger pads, and smoothly decay the weight function based on the distance to the closest finger pad, following roughly mechanoreceptor densities [30].…”

User Preferences for Calming Affective Haptic Stimuli in Social Settings

“…Social relaxation was split between 'Human' ( 9) with codes like 'Family' (3) and 'Friend' (3), and 'Animal' (7). The three coping mechanisms codes were 'Meditation' (8), 'Breathing Exercises' (6), and 'Mindfulness' (3).…”