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Quantitative Assessment of Hand Motor Function for Post-Stroke Rehabilitation Based on HAGCN and Multimodality Fusion | |
Li, Chenguang1,2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
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ISSN | 1534-4320 |
2022 | |
卷号 | 30页码:2032-2041 |
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. |
关键词 | Hemorrhaging Thumb Stroke (medical condition) Kinematics Feature extraction Wrist Correlation Hand motor function quantitative assessment multi-modality fusion graph convolutional network |
DOI | 10.1109/TNSRE.2022.3192479 |
关键词[WOS] | VIRTUAL ACTIVITIES ; STROKE ; RECOVERY |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation ofChina[U1913209] ; National Natural Science Foundation ofChina[62025307] ; Beijing Municipal Natural Science Foundation[JQ19020] |
项目资助者 | National Natural Science Foundation ofChina ; Beijing Municipal Natural Science Foundation |
WOS研究方向 | Engineering ; Rehabilitation |
WOS类目 | Engineering, Biomedical ; Rehabilitation |
WOS记录号 | WOS:000831114400003 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+医疗 |
国重实验室规划方向分类 | 智能能力评估 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49774 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Cheng, Long |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Control & Management Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Beijing Boai Hosp, China Rehabil Res Ctr, Beijing 100068, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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