高/低频的平衡带动了对嘈杂振动的触觉感知。

IF 2.4 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS IEEE Transactions on Haptics Pub Date : 2024-02-28 DOI:10.1109/TOH.2024.3371264
Corentin Bernard, Etienne Thoret, Nicolas Huloux, Solvi Ystad
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引用次数: 0

摘要

在对物体进行触觉探索的过程中传输的噪声振动触觉信号提供了有关物体表面性质的宝贵信息。了解信号特性与触觉系统如何解读信号之间的联系仍具有挑战性。在本文中,我们研究了人类对纹理探索过程中记录的宽带静态振动的感知,并使用振动触觉致动器进行了重现。由于强度是一种公认的感知属性,我们在此重点研究了光谱内容的相关性。首先对刺激的感知强度进行均衡,然后利用连续振动对之间的不相似性估计来识别最显著的频谱特征。基于维度缩小的频谱表征,差异评级模型显示,低频和高频之间的平衡是最重要的线索。通过 Mushra 实验对这一结果进行了正式验证,在该实验中,参与者评估了具有各种扭曲频率平衡的再合成振动的保真度。这些发现为人类的振动触觉感知提供了宝贵的见解,并建立了一个像人类一样分析振动的计算框架。此外,它们还为基于稀疏表示的信号合成和压缩铺平了道路,对涉及复杂振动反馈的应用具有重要意义。
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The high/low frequency balance drives tactile perception of noisy vibrations.

Noisy vibrotactile signals transmitted during tactile explorations of an object provide precious information on the nature of its surface. Understanding the link between signal properties and how they are interpreted by the tactile sensory system remains challenging. In this paper, we investigated human perception of broadband, stationary vibrations recorded during exploration of textures and reproduced using a vibrotactile actuator. Since intensity is a well-established perceptual attribute, we here focused on the relevance of the spectral content. The stimuli were first equalized in perceived intensity and subsequently used to identify the most salient spectral features using dissimilarity estimations between pairs of successive vibration. Based on dimensionally reduced spectral representations, models of dissimilarity ratings showed that the balance between low and high frequencies was the most important cue. Formal validation of this result was achieved through a Mushra experiment, in which participants assessed the fidelity of resynthesized vibrations with various distorted frequency balances. These findings offer valuable insights into human vibrotactile perception and establish a computational framework for analyzing vibrations as humans do. Moreover, they pave the way for signal synthesis and compression based on sparse representations, holding significance for applications involving complex vibratory feedback.

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来源期刊
IEEE Transactions on Haptics
IEEE Transactions on Haptics COMPUTER SCIENCE, CYBERNETICS-
CiteScore
5.90
自引率
13.80%
发文量
109
审稿时长
>12 weeks
期刊介绍: IEEE Transactions on Haptics (ToH) is a scholarly archival journal that addresses the science, technology, and applications associated with information acquisition and object manipulation through touch. Haptic interactions relevant to this journal include all aspects of manual exploration and manipulation of objects by humans, machines and interactions between the two, performed in real, virtual, teleoperated or networked environments. Research areas of relevance to this publication include, but are not limited to, the following topics: Human haptic and multi-sensory perception and action, Aspects of motor control that explicitly pertain to human haptics, Haptic interactions via passive or active tools and machines, Devices that sense, enable, or create haptic interactions locally or at a distance, Haptic rendering and its association with graphic and auditory rendering in virtual reality, Algorithms, controls, and dynamics of haptic devices, users, and interactions between the two, Human-machine performance and safety with haptic feedback, Haptics in the context of human-computer interactions, Systems and networks using haptic devices and interactions, including multi-modal feedback, Application of the above, for example in areas such as education, rehabilitation, medicine, computer-aided design, skills training, computer games, driver controls, simulation, and visualization.
期刊最新文献
Investigating the Kappa Effect Elicited Through Concurrent Visual and Tactile Stimulation. Two rapid alternatives compared to the staircase method for the estimation of the vibrotactile perception threshold. Multichannel Vibrotactile Glove: Validation of a new device designed to sense vibrations. Passive Realizations of Series Elastic Actuation: Effects of Plant and Controller Dynamics on Haptic Rendering Performance. VT-SGN:Spiking Graph Neural Network for Neuromorphic Visual-Tactile Fusion.
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