Parkinson Automatic Staging Based on Motion Sensors
YANG Yue1, WANG Feng1, SUN Feng2a, ZHENG Huifen2b
1.School of Biological Sciences & Medical Engineering, Southeast University, Nanjing Jiangsu 210000, China;
2.a.Department of Neurology; b.Department of Geriatric Neurology, Affiliated Brain Hospital, Nanjing Medical University, Nanjing
Jiangsu 210029, China
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%.
杨越1，汪丰1，孙丰2a，郑慧芬2b. 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.
YANG Yue1, WANG Feng1, SUN Feng2a, ZHENG Huifen2b. Parkinson Automatic Staging Based on Motion Sensors. China Medical Devices, 2018, 33(9): 37-41.
