Texture classification using a polymer-based MEMS tactile sensor

Sung Hoon Kim*, Jonathan Engel, Chang Liu, Douglas L. Jones

*Corresponding author for this work

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

We classify surface textures using a polymer-based microelectromechanical systems (MEMS) tactile sensor array and a robust statistical approach. We demonstrate that a MEMS tactile sensor resembling a flexible sensor 'skin' built using a polyimide substrate can successfully classify textures. Texture classification is achieved by using a maximum likelihood decision rule that optimally classifies patterns in the presence of noisy signal to cope with texture variation and random noise. Using a 4 × 4 sensor array, a variety of simple textures are distinguished despite low sensitivity mechanical strain gauges serving as a transduction element. The final result analyzed using leave-one-out cross validation yields acceptable overall performance of 68% correct classification. Directions for future work to improve identification performance of the system are also presented.

Original languageEnglish (US)
Pages (from-to)912-920
Number of pages9
JournalJournal of Micromechanics and Microengineering
Volume15
Issue number5
DOIs
StatePublished - May 1 2005

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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