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 ORCID:

Kezhou LIU

https://orcid.org/0000-0002-5235-7357

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Journal of Zhejiang University SCIENCE B 2022 Vol.23 No.8 P.625-641

http://doi.org/10.1631/jzus.B2100999


Research and application advances in rehabilitation assessment of stroke


Author(s):  Kezhou LIU, Mengjie YIN, Zhengting CAI

Affiliation(s):  Department of Biomedical Engineering, School of Automation (Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China

Corresponding email(s):   liukezhou@hdu.edu.cn

Key Words:  Stroke, Rehabilitation assessment, Stroke assessment scales, Detection technology, Artificial intelligence


Kezhou LIU, Mengjie YIN, Zhengting CAI. Research and application advances in rehabilitation assessment of stroke[J]. Journal of Zhejiang University Science B, 2022, 23(8): 625-641.

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DOI - 10.1631/jzus.B2100999


Abstract: 
stroke has a high incidence and disability rate, and rehabilitation is an effective means to reduce the disability rate of patients. To systematize rehabilitation assessment, which is the foundation for rehabilitation therapy, we summarize the assessment methods commonly used in research and clinical applications, including the various types of stroke rehabilitation scales and their applicability, and related biomedical detection technologies, including surface electromyography (sEMG), motion analysis systems, transcranial magnetic stimulation (TMS), magnetic resonance imaging (MRI), and combinations of different techniques. We also introduce some assessment techniques that are still in the experimental phase, such as the prospective application of artificial intelligence (AI) with optical correlation tomography (OCT) in stroke rehabilitation. This review provides a useful bibliography for the assessment of not only the severity of stroke injury, but also the therapeutic effects of stroke rehabilitation, and establishes a solid base for the future development of stroke rehabilitation skills.

脑中风康复评定方法的研究与应用进展

刘珂舟,印梦婕,蔡正厅
杭州电子科技大学自动化学院(人工智能学院),中国杭州市,310018
摘要:脑中风具有很高的发病率和致残率,而康复治疗是目前降低患者致残率的有效手段。为了系统化地进行康复评估,我们总结了研究和临床应用中常用的评估方法,包括各种类型的脑卒中康复量表及其适用性,以及相关的生物医学检测技术:表面肌电图、运动分析系统、经颅磁刺激、磁共振成像以及不同技术的组合。此外,我们还介绍了一些仍处于实验阶段的评估技术,如人工智能与光学相关断层扫描在中风康复的前瞻性应用。因此,本综述不仅为评估脑卒中损伤程度,也为评估脑卒中康复过程中的治疗效果提供了有价值的参考,同时为今后脑卒中康复技术的发展奠定了坚实的基础。

关键词:脑中风;康复评定;中风评估量表;检测技术;人工智能

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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