  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 \times
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 Na\"ive 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.
