Motion Classification Using Proposed Principle Component Analysis Hybrid K-Means Clustering

Ching Yee Yong, Rubita Sudirman, Nasrul Humaimi Mahmood, Kim Mey Chew

Infocomm Research Alliance, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia.

DOI: 10.4236/eng.2013.55B006                

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.

Keywords

Accelerometer; Gyroscope; Fuzzy; Bayes; Decision Tree

Conflicts of Interest

The authors declare no conflicts of interest.

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