中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data

文献类型:期刊论文

作者Cui, Chengkun1; Bian, Gui-Bin1; Hou, Zeng-Guang1,2,3; Zhao, Jun4; Su, Guodong4; Zhou, Hao4; Peng, Liang1; Wang, Weiqun1
刊名IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
出版日期2018-04-01
卷号26期号:4页码:856-864
关键词Post-stroke Hemiparesis Gait Analysis Multimodalfusion Marker Trajectory (Mt) Ground Reaction Force (Grf) Electromyogram (Emg)
DOI10.1109/TNSRE.2018.2811415
文献子类Article
英文摘要Gait analysis for the patients with lower limb motor dysfunction is a useful tool in assisting clinicians for diagnosis, assessment, and rehabilitation strategy making. Implementing accurate automatic gait analysis for the hemiparetic patients after stroke is a great challenge in clinical practice. This study is to develop a new automatic gait analysis system for qualitatively recognizing and quantitatively assessing the gait abnormality of the post-stroke hemiparetic patients. Twenty-one post-stroke patients and twenty-one healthy volunteers participated in thewalking trials. Three of the most representative gait data, i.e., marker trajectory (MT), ground reaction force (GRF), and electromyogram, were simultaneously acquired from these subjects during their walking. A multimodal fusion architecture is established by using these different modal data to qualitatively distinguish the hemiparetic gait from normal gait by different pattern recognition techniques and to quantitatively estimate the patient's lower limb motor function by a novel probability-based gait score. Seven decision fusion algorithms have been tested in this architecture, and extensive data analysis experiments have been conducted. The results indicate that the recognition performance and estimation performance of the system become better when more modal gait data are fused. For the recognition performance, the random forest classifier based on the GRF data achieves an accuracy of 92.26% outperformed other single-modal schemes. When combining two modal data, the accuracy can be enhanced to 95.83% by using the support vector machine (SVM) fusion algorithm to fuse the MT and GRF data. When integrating all the threemodal data, the accuracy can be further improved to 98.21% by using the SVM fusion algorithm. For the estimation performance, the absolute values of the correlation coefficients between the estimation results of the above three schemes and the Wisconsin gait scale scores for the post-stroke patients are 0.63, 0.75, and 0.84, respectively, which means the clinical relevance becomes more obvious when using more modalities. These promising results demonstrate that the proposedmethod has considerable potential to promote the future design of automatic gait analysis systems for clinical practice.
WOS关键词PATTERN-RECOGNITION ; CEREBRAL-PALSY ; NEURAL-NETWORK ; CLASSIFICATION ; INDEX ; OSTEOARTHRITIS ; MACHINES ; FEATURES
WOS研究方向Engineering ; Rehabilitation
语种英语
WOS记录号WOS:000429692400015
资助机构National Natural Science Foundation of China(61720106012 ; Beijing Natural Science Foundation(L172050) ; U1713220 ; 61533016 ; 61603386 ; 91648208 ; 61421004)
源URL[http://ir.ia.ac.cn/handle/173211/20964]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Hou, Zeng-Guang
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
4.Beijing Boai Hosp, China Rehabil Res Ctr, Beijing 100068, Peoples R China
推荐引用方式
GB/T 7714
Cui, Chengkun,Bian, Gui-Bin,Hou, Zeng-Guang,et al. Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2018,26(4):856-864.
APA Cui, Chengkun.,Bian, Gui-Bin.,Hou, Zeng-Guang.,Zhao, Jun.,Su, Guodong.,...&Wang, Weiqun.(2018).Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,26(4),856-864.
MLA Cui, Chengkun,et al."Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 26.4(2018):856-864.

入库方式: OAI收割

来源:自动化研究所

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