随机共振效应下的两指刚度判别

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING ACM Transactions on Applied Perception Pub Date : 2023-10-27 DOI:10.1145/3630254
Komi Chamnongthai, Takahiro Endo, Fumitoshi Matsuno
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引用次数: 0

摘要

我们研究了两根手指在随机共振中区分刚度的能力。我们知道,当振动触觉噪声传播到指尖时,指尖的触觉会得到改善,这种现象被称为随机共振。指尖触觉的改善取决于传播到指尖的噪声的强度。一种对多个指尖的触觉感觉的改进不需要将多个噪声源,如振动器附加到多个指尖;也就是说,即使是一个振动器也可以将噪声传播到多个手指。在本研究中,我们将刚度判别作为一项使用多个手指的任务,其中拇指和食指用于触摸物体并感知其刚度。随后,我们证明通过仅使用单个振动器向拇指和食指传播足够强烈的噪声可以改善刚度感知。研究结果表明,使用一个振动器可以改善多个指尖的触觉。
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Two-finger Stiffness Discrimination with the Stochastic Resonance Effect
We investigated the ability of two fingers to discriminate stiffness with stochastic resonance. It is known that the haptic perception at the fingertip improves when vibrotactile noise propagates to the fingertip, which is a phenomenon called the stochastic resonance. The improvement in the haptic sensation of a fingertip depends on the intensity of the noise propagating to the fingertip. An improvement in the haptic sensation of multiple fingertips does not require multiple noise sources, such as vibrators, to be attached to multiple fingertips; i.e., even a single vibrator can propagate noise to multiple fingers. In this study, we focus on stiffness discrimination as a task using multiple fingers, in which the thumb and index finger are used to touch an object and perceive its stiffness. Subsequently, we demonstrate that the stiffness perception is improved by propagating sufficiently intense noise to the thumb and index finger using only a single vibrator. The findings indicate the possibility of improving the haptic sensation at multiple fingertips using one vibrator.
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来源期刊
ACM Transactions on Applied Perception
ACM Transactions on Applied Perception 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
22
审稿时长
12 months
期刊介绍: ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields. The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.
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