中国医疗设备2018，Vol.33Issue(9)：37-41,5.DOI:10.3969/j.issn.1674-1633.2018.09.009
基于运动传感器的帕金森自动分级研究
Parkinson Automatic Staging Based on Motion Sensors
摘要
Abstract
The Hoehn-Yahr is the standard for the classification of Parkinson’s disease at present. Wearable devices based on motion sensors provide more objective and accurate monitoring for motor function evaluation of patients with Parkinson’s disease. This paper proposed an automatic grading algorithm based on six-axis acceleration and angular velocity sensor data for automatic classification of Parkinson’s disease. The algorithm used a combination of special motion parameters based on individual motion features and statistical parameters that were non-specific for each motion to model together. After obtaining the motion parameters, comparison of classification accuracy were conducted by using three current state-of-the-art machine learning algorithms such as support vector machine, K nearest neighbor, and random forests. At the same time, the influence of different parameters using different parameters on the classification accuracy was also analyzed. The final classification accuracy of the study in 67 individuals was 89.55%.关键词
运动传感器/帕金森病/机器学习/多分类/Hoehn-Yahr分期Key words
motion sensors/Parkinson’s disease/machine learning/multiple classifications/Hoehn-Yahr staging分类
医药卫生引用本文复制引用
杨越,汪丰,孙丰,郑慧芬..基于运动传感器的帕金森自动分级研究[J].中国医疗设备,2018,33(9):37-41,5.基金项目
中国自然科学基金(61127002,11572087, 3207037434) （61127002,11572087, 3207037434）
南京市医学科技发展资金资助项目(YKK17128). （YKK17128）
