Abstract: To achieve the comprehensive and accurate quantitative assessment of parkinsonian dyskinesia and assistant diagnosis of Parkinson’s Disease（PD）, this paper employes a new electronic walkway system used for quantifying gait analysis, the system contains the U-shape electronic walkway and the digital diagnosis system. Sensor signals are obtained from the U-shape electronic walkway. The gait kinematic characteristics related to the patient dyskinesia are extracted by the digital diagnosis system in straight and turning condition. In the testing phase, the Support Vector Machine（SVM） classification algorithm is used to identify the gait pattern of Parkinson’s disease patients. The experimental results indicate that based on the gait locomotion parameters obtained from electronic walkway, the constructed models not only achieve to recognize Parkinson’s disease patients, but also significantly improve the recognition accuracy by using the turning gait features.

Key words: Parkinson’s disease, gait analysis, U-shape electronic walkway system, Support Vector Machine（SVM）, turning gait feature

摘要： 为了实现帕金森病（PD）运动障碍全面而准确地量化评估和帕金森病的辅助诊断，设计一种新的用于量化步态分析的U-型电子步道系统，该系统包括U-型电子步道和数字诊疗系统。利用U-型电子步道系统获取传感器信号，并采用数字诊疗系统提取受试者直行和转弯状态下运动障碍相关的步态运动学特征。在测试阶段，采用支持向量机（SVM）分类算法识别帕金森病患者步态模式。实验结果表明基于U-型电子步道系统提取的步态运动学特征，构建的模型能实现帕金森病患者识别，同时提取的转弯步态特征能够大幅提高帕金病患者的准确率。

关键词: 帕金森病, 步态分析, U-型电子步道系统, 支持向量机, 转弯步态特征