Hardness Discrimination Using Piezoelectric-Based Biomimetic Tactile Sensor and Machine Learning

Abstract

In this letter, we present a tactile sensing system based on piezoelectric sensors, embedded electronics, and a machine learning (ML)-based approach for hardness discrimination. Various statistical features were extracted and evaluated through machine learning algorithms including support vector machines (SVM), single-layer feed-forward neural networks, and k-nearest neighbor (KNN). Five hardness objects were examined by performing indentation experiments using a Cartesian robot equipped with the sensing system while varying the indentation speed and load. Results showed that the SVM classifier trained on features ranked using principal component analysis (PCA) achieves a discrimination accuracy of 96% while utilizing a single sensor. Furthermore, results demonstrated that fixing the indentation speed and load increases the discrimination accuracy to 100%. This study demonstrated the capability of the tactile sensing system in extracting tactile information opening up interesting perspectives for wearable sensing and soft robots.