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.

Tactile Estimation of Molded Plastic Plates Based on the Estimated Impulse Responses of Mechanoreceptive Units.

This study proposes a tactile estimation method of molded plastic plates based on human tactile perception characteristics. Plastic plates are often used in consumer products. The tactile evaluation plays an important role in product development. However, physical quantities not taking into account human tactile perception have been employed in previous tactile estimation procedures. Hence, in this study, we adopted the vibrational thresholds of the mechanoreceptive units-FA I, FA II, SA I and SA II-for stimuli detection and developed a tactile estimation method for plastic plates that clarified the mechanoreceptive units related to tactile sensation. The developed tactile sensor consists of a base and a silicone rubber pad that contains strain gauges in it. We detected vibration during touch by the sensor and calculated the estimation of the firing values of the cutaneous mechanoreceptors, which are the essential data obtained by humans during tactile perception, in comparison to the amplitude spectrum of the vibration with the threshold amplitude of each mechanoreceptive unit. Simultaneously, we calculated the relationship between the normal and tangential forces recorded while the sensor ran over the samples. As a result of stepwise linear regression analysis using these values as explanatory variables, the evaluation scores for Soft were successfully estimated using the firing value of FA II and the relationship between normal/tangential forces, and the evaluation scores for Rough were estimated using the SA I firing value.