Yanwari Muhammad Irwan, Okamoto Shogo
Department of Computer Science, Tokyo Metropolitan University, Hino 1910065, Japan.
Department of Electrical Engineering, Politeknik Negeri Semarang, Kota Semarang 50275, Indonesia.
Sensors (Basel). 2025 Apr 20;25(8):2598. doi: 10.3390/s25082598.
Traditional tactile sensors primarily measure macroscopic surface features but do not directly estimate how humans perceive such surface roughness. Sensors that mimic human tactile processing could bridge this gap. This study proposes a method for predicting macroscopic roughness perception based on a sensing principle that closely resembles human tactile information processing. Humans are believed to assess macroscopic roughness based on the spatial distribution of subcutaneous deformation and resultant neural activities when touching a textured surface. To replicate this spatial-coding mechanism, we captured distributed contact information using a camera through a flexible, transparent material with fingerprint-like surface structures, simulating finger skin. Images were recorded under varying contact forces ranging from 1 N to 3 N. The spatial frequency components in the range of 0.1-1.0 mm were extracted from these contact images, and a linear combination of these components was used to approximate human roughness perception recorded via the magnitude estimation method. The results indicate that for roughness specimens with rectangular or circular protrusions of surface wavelengths between 2 and 5 mm, the estimated roughness values achieved an average error comparable to the standard deviation of participants' roughness ratings. These findings demonstrate the potential of macroscopic roughness estimation based on human-like tactile information processing and highlight the viability of vision-based sensing in replicating human roughness perception.
传统触觉传感器主要测量宏观表面特征，但不能直接估计人类如何感知这种表面粗糙度。模仿人类触觉处理的传感器可以弥补这一差距。本研究提出了一种基于与人类触觉信息处理非常相似的传感原理来预测宏观粗糙度感知的方法。人们认为，人类在触摸有纹理的表面时，会根据皮下变形的空间分布和由此产生的神经活动来评估宏观粗糙度。为了复制这种空间编码机制，我们通过具有指纹状表面结构的柔性透明材料，利用相机模拟手指皮肤，捕获分布式接触信息。在1 N至3 N的不同接触力下记录图像。从这些接触图像中提取0.1 - 1.0 mm范围内的空间频率分量，并使用这些分量的线性组合来近似通过量级估计法记录的人类粗糙度感知。结果表明，对于表面波长在2至5 mm之间的矩形或圆形凸起的粗糙度样本，估计的粗糙度值实现了与参与者粗糙度评级标准差相当的平均误差。这些发现证明了基于类人触觉信息处理进行宏观粗糙度估计的潜力，并突出了基于视觉的传感在复制人类粗糙度感知方面的可行性。
