Touch Position Detection in Electrical Tomography Tactile Sensors Through Quadratic Classifier

Abstract

Traditional electrical tomography tactile sensors consider the usage of the system's finite element model. This approach brings disadvantages that jeopardize their applicability aspect and wide use. To address this limitation, the main thrust of this paper is to present a method for touch position identification for an electrical tomography flexible tactile sensor. This is done by using a supervised machine learning algorithm for performing classification, namely quadratic discriminant analysis. This approach provides accurate contact location identification, increasing the detection speed and the sensor versatility when compared with traditional electrical tomography approaches. Results obtained show classification accuracy rates of up to 91.6% on unseen test data and an average Euclidean error ranging from 1 to 10 mm depending on the contact location over the sensor. The sensor is then applied in real case scenarios to show its efficiency. These outcomes are encouraging since they promote the future practical usage of flexible and low-cost sensors.