期刊
JOURNAL OF MICROMECHANICS AND MICROENGINEERING
卷 15, 期 5, 页码 912-920出版社
IOP PUBLISHING LTD
DOI: 10.1088/0960-1317/15/5/003
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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 x 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.
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