Wen Junli, Yang Xianjun, Xu Shengqiang, Liu Yao, Tang Zheng, Qiu Xiaolei, Xie Nanjyu, Sun Yining
Institute of Intelligent Machines, Chinese Academy of Science, Hefei, 230031.
University of Science and Technology of China, Hefei, 230026.
Zhongguo Yi Liao Qi Xie Za Zhi. 2017 Nov 30;41(6):415-418. doi: 10.3969/j.issn.1671-7104.2017.06.007.
In order to evaluate the ability of Parkinson's patients to walk comprehensively, a system based on MEMS to aid clinical quantification of ability in Parkinson's is established. The inertial units are respectively fixed on the back and the waist of subject to be measured. The Kalman fusion algorithm is used to extract the characteristic parameters of accelerometer and gyroscope data. SVM classifier is designed to train and test the classifier by the feature. The results show that the system possesses a high recognition rate for Parkinson's patients and normal subjects and for the classification of the walking ability of patients with Parkinson's disease. So, this system can aid doctors to give more object diagnostic conclusion.
