Hardness estimation based on human sensorimotor control with a flexible wearable tactile sensor

Abstract

In this paper, we used a flexible wearable tactile sensor that utilizes acoustic reflection, and investigated hardness estimation based on the sensor output using human spontaneous haptic exploratory motion. Machine learning methods with support vector machine and convolutional neural networks were adopted to the acquired sensor output for the identification of hardness of cervical models. As a result, the correct answer rates of both learning models were 100 ％ for the soft model and about 80 ％ for the hard and medium models.