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.

Conditional Generative Adversarial Network-Based Tactile Stimulus Generation for Ultrasonic Tactile Display

“…3. A tactile sensor equipped with strain gauges inside it [10] is mounted on a friction tester (TL201s, Trinity-Lab Inc., Japan). The tactile sensor consists of a two-layer silicone rubber pad with different hardness, and two strain gauges are attached to phosphor bronze plates in each layer.…”

“…To reproduce tactile sensations, technologies to quantify the tactile sensation of an object and to display tactile sensations using a tactile display are necessary. To quantify tactile sensation, it has been suggested that focusing on vibration information at the time of object touch is an effective approach given the human tactile perception mechanism [8], [9], [10], [11]. Tactile displays…”

“…Therefore, haptic rendering via tactile sensation, that is, the conversion from sensed vibration to tactile sensation and from tactile sensation to input to an ultrasonic tactile display, is needed. For this tactile rendering, previous studies have addressed the conversion from sensed vibration to tactile sensation [8], [9], [10], [11]. This study considers the conversion from tactile sensation to display input, and focuses on conditional generative adversarial networks (CGANs) [16], [17], which are a type of generative adversarial network (GAN) [18], [19], as the conversion method.…”

Nonlinear Tactile Estimation Model based on Perceptibility of Mechanoreceptors improves Quantitative Tactile Sensing

“…Another approach is to extract meaningful information from vibration data obtained by tracing a simple vibration sensor on an object [16][17][18][19][20][21][22]. Because the four types of mechanoreceptors in our body have different frequency characteristics [23][24][25], they work as lters for vibration stimulations on the skin.…”

“…This means that the meaningful information for humans is not the entire vibration data but is hidden in the vibration data. Previous research [16][17][18][19] focused on extracting meaningful information; we believe that it is quite promising because the information or feature quantities are often extracted based on the characteristics of human mechanoreceptors. By doing so, it becomes possible to interpret tactile sensation based on human perceptive characteristics.…”