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Features Extraction for Classifying Parkinson's Disease Based on Gait Analysis  

Lee, Sang-Hong (경원대학교 전자계산학과)
Lim, Joon-S. (경원대학교 소프트웨어학부)
Shin, Dong-Kun (삼육대학교 컴퓨터학부)
Publication Information
Journal of Internet Computing and Services / v.11, no.6, 2010 , pp. 13-20 More about this Journal
Abstract
This paper presents a measure to classify healthy persons and Parkinson disease patients from the foot pressure of healthy persons and that of Parkinson disease patients using gait analysis based characteristics extraction and Neural Network with Weighted Fuzzy Membership Functions (NEWFM). To extract the inputs to be used in NEWFM, in the first step, the foot pressure data provided by the PhysioBank and changes in foot pressure over time were used to extract four characteristics respectively. In the second step, wavelet coefficients were extracted from the eight characteristics extracted from the previous stage using the wavelet transform (WT). In the final step, 40 inputs were extracted from the extracted wavelet coefficients using statistical methods including the frequency distribution of signals and the amount of variability in the frequency distribution. NEWFM showed high accuracy in the case of the characteristics obtained using differences between the left foot pressure and the right food pressure and in the case of the characteristics obtained using differences in changes in foot pressure over time when healthy persons and Parkinson disease patients were classified by extracting eight characteristics from foot pressure data. Based on these results, the fact that differences between the left and right foot pressures of Parkinson disease patients who show a characteristic of dragging their feet in gaits were relatively smaller than those of healthy persons could be identified through this experiment.
Keywords
Parkinson's disease; Gait; Fuzzy Neural Networks; Wavelet Transforms; Feature Extraction;
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Times Cited By KSCI : 5  (Citation Analysis)
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