Online Direction Classification and Recognition Using an Intuitive Tactile Communication

Abstract:

This paper proposed an online direction classifying method for constructing an intuitive tactile communication during human-robot cooperation. The proposed approach abstracts a suitable feature set from a tactile array sensor equipped on a hand-bar. This lower computation feature extraction method analyze the weighting values concerned with oriental information from principle component analysis (PCA), together with support vector machines (SVM) classifier for direction classification and recognition. Experimental results showed an average accuracy of 96.3% and a low costs of 512μs with respect to different handle gestures of the 6 touch directions, which is practicable utilized for human-robot cooperation based on tactile recognition.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 1542-1545
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.1542
• Online since: March 2014
• Authors: Hao Ying Wu*, Kai Fan
• Keywords: Feature Extraction, Human-Robot Cooperation, Tactile Recognition, Tactile Sensing

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