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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4719

Title: A Reduced Classifier Ensemble Approach to Human Gesture Classification for Robotic Chinese Handwriting
Authors: Fei Chao
Yan Sun
Zhengshuai Wang
Gang Yao
Zuyuan Zhu
Changle Zhou
Qinggang Meng
Min Jiang
Issue Date: 2013
Abstract: The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character’s strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble’s size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke’s trajectory to the robot’s motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters.
URI: http://hdl.handle.net/123456789/4719
Source URI: http://dspace.xmu.edu.cn:8080/dspace/handle/2288/79886
Source Fulltext: http://dspace.xmu.edu.cn
Appears in Collections:OAPS-大学生创新计划项目

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