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高/低频平衡驱动对嘈杂振动的触觉感知。

The High/Low Frequency Balance Drives Tactile Perception of Noisy Vibrations.

作者信息

Bernard Corentin, Thoret Etienne, Huloux Nicolas, Ystad Solvi

出版信息

IEEE Trans Haptics. 2024 Oct-Dec;17(4):614-624. doi: 10.1109/TOH.2024.3371264. Epub 2024 Dec 19.

Abstract

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.

摘要

在对物体进行触觉探索时传输的嘈杂振动触觉信号提供了有关其表面性质的宝贵信息。理解信号特性与触觉感觉系统如何对其进行解释之间的联系仍然具有挑战性。在本文中,我们研究了人类对在纹理探索过程中记录并使用振动触觉致动器再现的宽带、平稳振动的感知。由于强度是一个已确立的感知属性,我们在此专注于频谱内容的相关性。首先将刺激在感知强度上进行均衡,然后使用连续振动对之间的差异估计来识别最显著的频谱特征。基于降维后的频谱表示,差异评级模型表明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失真频率平衡的重新合成振动的保真度。这些发现为人类振动触觉感知提供了有价值的见解,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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