title: Artificial skin ridges enhance local tactile shape discrimination.
abstract: One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects' shapes and, more specifically, the objects' surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 × 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Naïve Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared for various parameters. Finally, the most accurate method was selected to evaluate the proposed skin structure in recognition of three different curvatures. The results showed an accuracy rate of 97.5% in surface curvature discrimination.
authors.affiliation: Social Robotics Lab, Interactive and Digital Media Institute, Department of Electrical and Computer Engineering, National University of Singapore, 21 Heng Mui Keng Terrace, 119613 Singapore. saba.salehi@gmail.com
authors.forename: Saba
authors.initials: S
authors.lastname: Salehi
authors.forename: John-John
authors.initials: JJ
authors.lastname: Cabibihan
authors.forename: Shuzhi Sam
authors.initials: SS
authors.lastname: Ge
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D016571:Neural Networks, Computer; D012867:Skin / Q000033:anatomy & histology*; D060388:Support Vector Machine; D014110:Touch