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 ENG  Vol.5 No.5 B , May 2013
Motion Classification Using Proposed Principle Component Analysis Hybrid K-Means Clustering
Abstract: This study investigates and acts as a trial clinical outcome for human motion and behaviour analysis in consensus of health related quality of life in Malaysia. The proposed technique was developed to analyze and access the quality of human motion that can be used in hospitals, clinics and human motion researches. It aims to establish how to widespread the quality of life effects of human motion. Reliability and validity are needed to facilitate subject outcomes. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behaviour. Five classifiers and algorithms were used to recognize and classify the motion patterns. The proposed PCA-K-Means clustering took 0.058 seconds for classification process. Resubstitution error for the proposed technique was 0.002 and achieved 94.67% of true positive for total confusion matrix of the classification accuracy. The proposed clustering algorithm achieved higher speed of processing, higher accuracy of performance and reliable cross validation error.
Cite this paper: C. Y. Yong, R. Sudirman, N. H. Mahmood and K. M. Chew, "Motion Classification Using Proposed Principle Component Analysis Hybrid K-Means Clustering," Engineering, Vol. 5 No. 5B, 2013, pp. 25-30. doi: 10.4236/eng.2013.55B006.
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