Adapting robot kinematics for human-arm motion recognition

Chan, C.S. and Liu, H. and Brown, D. (2007) Adapting robot kinematics for human-arm motion recognition. International Journal of Knowledge-based and Intelligent Engineering Systems, 11 (4). pp. 207-217. ISSN 1327-2314,

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This paper presents a novel method to the analysis of human-arm motion, in particular improving the efficiency of conventional motion recognition algorithms. Contrary to the prior art methods, this research develops a framework for human-arm motion recognition where qualitative normalised templates (QNTs) is proposed to replace the conventional approaches. First of all, the conventional robotic model has been employed to build a generic vision model for a human-arm, that is we utilise the robot kinematics to construct a stick model. Secondly, the qualitative robotic model is adopted to learn and construct the QNTs where human-arm motion is termed as, whose execution is consistent and could be easily characterised by a definite space-time trajectory in configuration space. Finally, classification of the human-arm motion is achieved by comparing the QNTs to the parameters learnt with particle filter based motion tracking algorithm. Experimental evaluation has demonstrated the effectiveness of the proposed method in human-arm motion classification, and our future work is focused on extending the proposed method to recognise complex human motion, e.g. walking and running.

Item Type: Article
Uncontrolled Keywords: Motion classification, pattern recognition, kinematics model
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Miss Nur Jannatul Adnin Ahmad Shafawi
Date Deposited: 15 Apr 2013 01:49
Last Modified: 15 Apr 2013 01:49

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