• Chia Min Lim Faculty of Computer and Informatics Multimedia University, Malaysia
  • Hu Ng Faculty of Computer and Informatics Multimedia University, Malaysia
  • Timothy Tzen Vun Yap Faculty of Computer and Informatics Multimedia University, Malaysia
  • Chiung Ching Ho Faculty of Computer and Informatics Multimedia University, Malaysia



Parkinson disease, gait, classification


The objective of this paper is to analyse the gait of subjects with suffering Parkinson's Disease (PD), plus to differentiate their gait from those of normal people. The data is obtained from a medical gait database known as Gaitpdb [1]. In the data set, there are 73 control subjects and 93 subjects with PD. In our study, we first obtained the gait features using statistical analysis, which include minimum, maximum, median, kurtosis, mean, skewness, standard deviation and average absolute deviation of the gait signal. Next, selection of the extracted features is performed using PSO search, Tabu search and Ranker. Finally the selected features will undergo classification using BFT, BPANN, k-NN, SVM with Ln kernel, SVM with Poly kernel and SVM with Rbf kernel. From the experimental results, the proposed model achieved average of 66.43%, 89.97%, 87.00%, 88.47%, 86.80% and 87.53% correct classification rates respectively.


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How to Cite

Min Lim, C., Ng, H., Tzen Vun Yap, T., & Ching Ho, C. (2015). GAIT ANALYSIS AND CLASSIFICATION ON SUBJECTS WITH PARKINSON’S DISEASE. Jurnal Teknologi, 77(18).