Tactile Evaluation Feedback System for Multi-Layered Structure Inspired by Human Tactile Perception Mechanism

Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing

Tactile sensing has attracted significant attention as a tactile quantitative evaluation method because the tactile sensation is an important factor while evaluating consumer products. Although the human tactile perception mechanism has nonlinearity, previous studies have often developed linear regression models. In contrast, this study proposes a nonlinear tactile estimation model that can estimate sensory evaluation scores from physical measurements. We extracted features from the vibration data obtained by a tactile sensor based on the perceptibility of mechanoreceptors. In parallel, a sensory evaluation test was conducted using 10 evaluation words. Then, the relationship between the extracted features and the tactile evaluation results was modeled using linear/nonlinear regressions. The best model was concluded by comparing the mean squared error between the model predictions and the actual values. The results imply that there are multiple evaluation words suitable for adopting nonlinear regression models, and the average error was 43.8% smaller than that of building only linear regression models.

A Model for Estimating Tactile Sensation by Machine Learning Based on Vibration Information Obtained while Touching an Object

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.

Modeling the basic behaviors of Anesthesia Training in Relation to Puncture and Penetration Feedback

Failure rates in spinal anesthesia are generally low in experienced hands. However, studies report a failure rate variation of 1% to 17% in this procedure. The aim of this study is to bring the main characteristics of in vivo procedure to the virtual reality simulated environment. The first step is to model the behavior of tissue layers being punctured by a needle to then make its inclusion in medical training possible. The simulation proposed here is implemented using a Phantom Omni haptic device. Every crucial sensation of the method mentioned here was assessed by a dozen volunteers who participated in two experiments designed to validate the modeled response. Each user answered six questions (three for each experiment). Good results were achieved in certain essential aspects of the process, such as identifying the number of layers, the most rigid layer to puncture, and the most resistant layers to pass through. These results indicated that it is possible to represent many typical behaviors through virtual needle insertion in spinal anesthesia with the correct use of haptic properties.Clinical relevance- The idea is to create a spinal anesthesia simulator that could work as a complementary step in training new anesthetists. The use of a simulator avoids introducing the first puncture haptic sensation directly in patients.

Physical origin of a complicated tactile sensation: 'shittori feel'

Shittori feel is defined as a texture that is moderately moisturized; however, many people experience 'shittori feel' when they touch a dry solid material containing little liquid. Here, shittori feel was evaluated for 12 materials. We found that the highest score of shittori feel was achieved by powders. Multiple regression analysis showed that shittori feel is a complex sense of moist and smooth feels. We analysed the relationship between the physical properties and the moist/smooth feels to show how subjects felt certain feels simultaneously. The moist and smooth feels are related to the surface roughness and friction characteristics of the materials. The moist and smooth feels can be perceived when the finger starts to move on the material surface and when the finger moves and rubs the material surface, respectively.

Modeling Semantically Multilayered Affective and Psychophysical Responses Toward Tactile Textures

Subjective responses related to touch are described via perceptual and affective adjectives. Understanding relationships among such responses is useful for industrial surface design. We developed a method for investigating relationships among touch-related responses by constructing multilayered adjective models without assumptions regarding model structure such as adjective hierarchy or a definition of layers. The method consists of two processes through which a model structure and parameters are estimated. To acquire the model structure, subjectively evaluated causal relationships among adjective pairs are used. Free parameters, such as effects among adjectives, are then statistically estimated through sensory evaluation and structural equation modeling. To validate the method, two models with different levels of details were developed using 29 adjective pairs and 46 texture samples. For example, the model with three layers, which we categorized into psychophysical, affective, and hedonic layers, was constructed. Moreover, a detailed model with four layers was provided, which was more complex than previously reported models. The present method helps advance our understanding and design of connections among subjective surface ratings.