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Object shape discrimination using sensorized glove | IEEE Conference Publication | IEEE Xplore

Object shape discrimination using sensorized glove


Abstract:

The sense of touch is crucial for distinguishing different shapes like boxes, cylinders and spheres. This paper proposes a machine learning based model to distinguish bet...Show More
Notes: PDF Not Yet Available In IEEE Xplore. The document that should appear here is not currently available. IEEE Xplore is working to obtain a replacement PDF. That PDF will be posted as soon as it is available. We regret any inconvenience in the meantime.

Abstract:

The sense of touch is crucial for distinguishing different shapes like boxes, cylinders and spheres. This paper proposes a machine learning based model to distinguish between shapes using kinesthetic information from the joint angles of the fingers. The training data from a sensorized glove was used to train a multi-layer support vector machine with a radial basis kernel. When used on simple shapes the proposed model obtained 100% accuracy. The accuracy dropped down to 71% when it was trained with shapes held in more than one way. The joints of the fingers that were critical in holding a particular shape was also identified in this paper.
Notes: PDF Not Yet Available In IEEE Xplore. The document that should appear here is not currently available. IEEE Xplore is working to obtain a replacement PDF. That PDF will be posted as soon as it is available. We regret any inconvenience in the meantime.
Date of Conference: 12-14 June 2013
Date Added to IEEE Xplore: 22 July 2013
ISBN Information:

ISSN Information:

Conference Location: Hangzhou, China