Perceptual processes are frequently multi-modal. This is the case of haptic perception. Data sets of visual and haptic sensory signals have been compiled in the past, especially when it comes to the exploration of textured surfaces. These data sets were intended to be used in natural and artificial perception studies and to provide training data sets for machine learning research. These data sets were typically acquired with rigid probes or artificial robotic fingers. Here, we collected visual, auditory, and haptic signals acquired when a human finger explored textured surfaces. We assessed the data set via machine learning classification techniques. Interestingly, multi-modal classification performance could reach 97% when haptic classification was around 80%.

翻译：感知过程通常是多模态的，触觉感知即为一例。过去已汇编了多种视觉与触觉信号数据集，尤其针对纹理表面的探索。这些数据集旨在用于自然与人工感知研究，并为机器学习研究提供训练数据。以往的数据集通常通过刚性探针或人工机械手指获取。本研究采集了人类手指探索纹理表面时的视觉、听觉与触觉信号，并通过机器学习分类技术对数据集进行了评估。值得注意的是，当触觉分类准确率约为80%时，多模态分类性能可达97%。