Next Article in Journal
Ethanol Vapor Sensing Properties of Triangular Silver Nanostructures Based on Localized Surface Plasmon Resonance
Next Article in Special Issue
Selective Change Driven Imaging: A Biomimetic Visual Sensing Strategy
Previous Article in Journal
Conductometric Sensors for Monitoring Degradation of Automotive Engine Oil
Previous Article in Special Issue
An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways
Article Menu

Export Article

Open AccessArticle
Sensors 2011, 11(9), 8626-8642; doi:10.3390/s110908626

Artificial Skin Ridges Enhance Local Tactile Shape Discrimination

Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576 Singapore
Author to whom correspondence should be addressed.
Received: 9 August 2011 / Revised: 31 August 2011 / Accepted: 2 September 2011 / Published: 5 September 2011
(This article belongs to the Special Issue Biomimetic Sensors, Actuators and Integrated Systems)
View Full-Text   |   Download PDF [443 KB, uploaded 21 June 2014]   |  


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. View Full-Text
Keywords: tactile sensing; curvature discrimination; local shape; ridged skin cover; fingerprints; robotic hand; prosthetic hand; machine learning; support vector machines tactile sensing; curvature discrimination; local shape; ridged skin cover; fingerprints; robotic hand; prosthetic hand; machine learning; support vector machines

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Salehi, S.; Cabibihan, J.-J.; Ge, S.S. Artificial Skin Ridges Enhance Local Tactile Shape Discrimination. Sensors 2011, 11, 8626-8642.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top