A model for estimating tactile sensation by machine learning based on vibration information obtained while touching an object

Abstract

The tactile sensation is an important indicator of the added value of a product, and it is thus important to be able to evaluate this sensation quantitatively. Sensory evaluation is generally used to quantitatively evaluate the tactile sensation of an object. However, statistical evaluation of the tactile sensation requires many participants and is, thus, time-consuming and costly. Therefore, tactile sensing technology, as opposed to sensory evaluation, is attracting attention. In establishing tactile sensing technology, it is necessary to estimate the tactile sensation of an object from information obtained by a tactile sensor. In this research, we developed a tactile sensor made of two-layer silicone rubber with two strain gauges in each layer and obtained vibration information as the sensor traced an object. We then extracted features from the vibration information using deep autoencoders, following the nature of feature extraction by neural firing due to vibrations perceived within human fingers. We also conducted sensory evaluation to obtain tactile scores for different words from participants. We finally developed a tactile sensation estimation model for each of the seven samples and evaluated the accuracy of estimating the tactile sensation of unknown samples. We demonstrated that the developed model can properly estimate the tactile sensation for at least four of the seven samples.