关键词: 步态分析, 非平衡数据, 电子步道系统, 帕金森病, 代价敏感支持向量机

Abstract: Dyskinesia is the significant feature of Parkinson’s disease patient, therefore the data analysis on parkinsonian gait kinematic parameters can provide a strong basis for disease diagnosis and rehabilitation. The actual number of Parkinson’s disease is far less than the normal human. The traditional machine learning methods are not suitable to classify the imbalanced data directly. This paper proposes a Cost Sensitive Support Vector Machine（CS-SVM） approach to distinguish between healthy human and patients with Parkinson’s disease firstly. The entire subject’s gait data is extracted from real U-shape electronic walkway. The classification performance is also improved by transforming the extracting features into non-dimensional forms. The experimental results indicate that the CS-SVM method can achieve the prediction accuracy of 94.16%, and the F-measure value reaches 87.08%, while it achieves a better performance comparing traditional SVM and the recognition performance is significantly improved by eliminating the influence of height, the prediction accuracy and F-measure value reach 94.81% and 88.66% respectively.

Key words: gait analysis, imbalanced data, electronic walkway system, Parkinson’s disease, Cost Sensitive Support Vector Machine（CS-SVM）