Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion

doi:10.1109/TNSRE.2022.3192479

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion
	Li, Chenguang 2,3; Yang, Hongjun 3; Cheng, Long 2,3; Huang, Fubiao 1; Zhao, Shuang 1; Li, Dongyue 1; Yan, Ruxiu 1
刊名	IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
	2022
卷号	30 页码:2032-2041
关键词	Hemorrhaging Thumb Stroke (medical condition) Kinematics Feature extraction Wrist Correlation Hand motor function quantitative assessment multi-modality fusion graph convolutional network
ISSN号	1534-4320
DOI	10.1109/TNSRE.2022.3192479
通讯作者	Cheng, Long(long.cheng@ia.ac.cn)
英文摘要	Quantitative assessment of hand function can assist therapists in providing appropriate rehabilitation strategies, which plays an essential role in post-stroke rehabilitation. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. To quantitatively assess the hand motor function of patients with post-stroke hemiplegia, this study proposes a novel multi-modality fusion assessment framework. This framework includes three components: the kinematic feature extraction based on a graph convolutional network (HAGCN), the surface electromyography (sEMG) signal processing based on a multi-layer long short term memory (LSTM) network, and the quantitative assessment based on the multi-modality fusion. To the best of the authors' knowledge, this is the first study of applying a graph convolution network to assess the hand motor function. We also collect the kinematic data and sEMG data from 70 subjects who completed 28 types of hand movements. Therapists first graded patients using traditional motor assessment scales (Brunnstrom Scale and Fugl-Meyer Assessment Scale) and further refined the patient's motor assessment result by their experience. Then, we trained the HAGCN and LSTM networks and quantitatively assessed each patient based on the proposed assessment framework. Finally, the Spearman correlation coefficient (SC) between the assessment result of this study and the traditional scale are 0.908 and 0.967, demonstrating a significant correlation between the proposed assessment and the traditional scale scores. In addition, the SC value between the score of this study and the refined hand motor function is 0.997, indicating the "ceiling effect" of some traditional scales can be avoided.
资助项目	National Natural Science Foundation ofChina[U1913209] ; National Natural Science Foundation ofChina[62025307] ; Beijing Municipal Natural Science Foundation[JQ19020]
WOS关键词	VIRTUAL ACTIVITIES ; STROKE ; RECOVERY
WOS研究方向	Engineering ; Rehabilitation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000831114400003
资助机构	National Natural Science Foundation ofChina ; Beijing Municipal Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49773]
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	Cheng, Long
作者单位	1.Beijing Boai Hosp, China Rehabil Res Ctr, Beijing 100068, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Control & Management Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Chenguang,Yang, Hongjun,Cheng, Long,et al. Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2022,30:2032-2041.
APA	Li, Chenguang.,Yang, Hongjun.,Cheng, Long.,Huang, Fubiao.,Zhao, Shuang.,...&Yan, Ruxiu.(2022).Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,30,2032-2041.
MLA	Li, Chenguang,et al."Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 30(2022):2032-2041.