Neural-Network-Based Tactile Perception System Using Ultrahigh-Resolution Tactile Sensor
Neural-Network-Based Tactile Perception System Using Ultrahigh-Resolution Tactile Sensor
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DOI：
10.1109/toh.2023.3269797
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发表时间：
2023-10-01
影响因子：
2.9
通讯作者：
Takao，Hidekuni
中科院分区：
文献类型：
--
作者：
Maeda，Yusaku;Tanimoto，Kei;Takao，Hidekuni
在这项研究中，我们开发了第一个触觉感知系统的感官评价的基础上的微机电系统（MEMS）触觉传感器的分辨率超过人类指尖。采用语义区分法，以“光滑”等6个评价词对17种织物进行感官评价。触觉信号以1 µm的空间分辨率获得;每种织物的总数据长度为300 mm。使用卷积神经网络作为回归模型实现感官评价的触觉感知。使用未用于训练的数据作为未知织物来评估系统的性能。首先，我们得到了均方误差（MSE）与输入数据长度${\bm{L}}$的关系。在${\bm{L }} = {\bm{\ }}$300 mm时，MSE为0.27。在${\bm{L }} = {\bm{\ }}$300 mm处成功预测了89.2%的评价词。已实现了一种能够定量比较新织物与现有织物的触感的系统。此外，织物的区域影响由热图可视化的每个触觉，这可以导致用于实现理想产品触觉的设计策略。
In this study, we developed the first tactile perception system for sensory evaluation based on a microelectromechanical systems (MEMS) tactile sensor with an ultrahigh resolution exceeding than that of a human fingertip. Sensory evaluation was performed on 17 fabrics using a semantic differential method with six evaluation words such as “smooth”. Tactile signals were obtained at a spatial resolution of 1 µm; the total data length of each fabric was 300 mm. The tactile perception for sensory evaluation was realized with a convolutional neural network as a regression model. The performance of the system was evaluated using data not used for training as unknown fabric. First, we obtained the relationship of the mean squared error (MSE) to the input data length ${\bm{L}}$. The MSE was 0.27 at ${\bm{L }} = {\bm{\ }}$300 mm. Then, the sensory evaluation and model estimated scores were compared; 89.2% of the evaluation words were successfully predicted at ${\bm{L }} = {\bm{\ }}$300 mm. A system that enables the quantitative comparison of the tactile sensation of new fabrics with existing fabrics has been realized. In addition, the region of the fabric affects each tactile sensation visualized by a heatmap, which can lead to a design policy for achieving the ideal product tactile sensation.