Neural-Network-Based Tactile Perception System Using Ultrahigh-Resolution Tactile Sensor

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.