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2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 2330-2332
Xi Wu , School of Computer and Information, Hefei University of Technology, Hefei, China
Xu Chen , School of Computer and Information, Hefei University of Technology, Hefei, China
You Duan , School of Computer and Information, Hefei University of Technology, Hefei, China
Shengqiang Xu , Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China
Nan Cheng , Hospital Affiliated to Institute of Neurology, Anhui University of Chinese Medicine, Hefei, China
Ning An , School of Computer and Information, Hefei University of Technology, Hefei, China
ABSTRACT
Gait analysis aims to study human motion and its potential association with chronic diseases, such as Parkinson's disease and hemiplegic paralysis, by extracting various gait characteristics. It has been a challenging problem to accurately extract temporal and spatial gait parameter and to explore the relationship between gait signal and a disease of interest. In this study, we introduce a gait sensing platform that can capture human movement and classify patients with Parkinson's disease from healthy subjects. Specifically, we first show the platform that consists of force sensitive pressure sensors. Second, we extract gait features from the gait signal collected from the platform. Finally, we collect experimental data from 386 volunteers, including 218 healthy subjects and 168 patients with Parkinson's disease, and conduct extensive experiments to show the possibility of classifying Parkinson's disease patients at a high confidence level. Experimental results of nine different classifiers show that the random forest model outperforms the other eight competitors and obtains an accuracy of 92.49%, demonstrating the power of quantitative gait analysis in the early detection of Parkinson's disease.
INDEX TERMS
Parkinson's disease, Feature extraction, Sensors, Legged locomotion, Support vector machines, Force
CITATION

X. Wu, X. Chen, Y. Duan, S. Xu, N. Cheng and N. An, "A study on gait-based Parkinson's disease detection using a force sensitive platform," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 2330-2332.
doi:10.1109/BIBM.2017.8218048
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