Development of Pinching Motion Classification Method Using EIT-Based Tactile Sensor

Abstract

Fine motor skills have been suggested to be related to human cognitive abilities. To develop an objective method for evaluating fine motor skills, we applied a flexible tactile sensor based on electrical impedance tomography (EIT) and the contact resistance principle to a cylinder designed to mimic the peg used in the Functional Dexterity Test. Six pinching motions were classified to confirm the feasibility of the prototype system. Two types of classification were performed: classification using reconstructed images and classification using measured voltage vectors. The feasibility of the classification method was evaluated using adult subjects, and it was demonstrated that the system can accurately classify various types of pinching motions. The results revealed that utilizing reconstructed images for classification achieved a discrimination accuracy of 79.4%, while employing measured voltage vectors for classification resulted in a discrimination accuracy of 91.4%. These findings underscore the potential for developing an automated finger motion analysis system using EIT-based tactile sensor.