中国组织工程研究

中国组织工程研究 ›› 2023, Vol. 27 ›› Issue (8): 1257-1263.doi: 10.12307/2023.088

• 组织构建综述 tissue construction review • 上一篇    下一篇

脑卒中后下肢步态分析的方法与应用

于文强1，任富超1，石国宏2，许苑晶3，刘同有2，谢幼专2，王金武2，3   

  1. 1潍坊医学院康复医学院，山东省潍坊市  261053；2上海市骨科内植物重点实验室，上海交通大学医学院附属第九人民医院骨科，上海市 200011；3上海交通大学生物医学工程学院，上海市  200030
  • 收稿日期:2022-03-07 接受日期:2022-04-29 出版日期:2023-03-18 发布日期:2022-07-28
  • 通讯作者: 王金武，博士，教授，主任医师，上海市骨科内植物重点实验室，上海交通大学医学院附属第九人民医院骨科，上海市 200011；上海交通大学生物医学工程学院，上海市 200030
  • 作者简介:于文强，男，1997年生，汉族，安徽省人，潍坊医学院在读硕士，主要从事3D打印康复辅具的研究。
  • 基金资助:
    国家科技部重点研发计划项目(2018YFC2002303)，项目负责人：谢幼专；上海市科委项目(19441908700)，项目负责人：王金武；上海交通大学医学院地高大双百人计划 (20152224)，项目负责人：王金武；上海交通大学医学院附属第九人民医院临床研究型MDT项目(201914)，项目负责人：王金武

Methods and application of gait analysis of lower limbs after stroke

Yu Wenqiang1, Ren Fuchao1, Shi Guohong2, Xu Yuanjing3, Liu Tongyou2, Xie Youzhuan2, Wang Jinwu2, 3   

  1. 1School of Rehabilitation Medicine, Weifang Medical University, Weifang 261053, Shandong Province, China; 2Shanghai Key Laboratory of Orthopedic Endophysiology, Department of Orthopedics, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; 3School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
  • Received:2022-03-07 Accepted:2022-04-29 Online:2023-03-18 Published:2022-07-28
  • Contact: Wang Jinwu, MD, Professor, Chief physician, Shanghai Key Laboratory of Orthopedic Endophysiology, Department of Orthopedics, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
  • About author:Yu Wenqiang, Master candidate, School of Rehabilitation Medicine, Weifang Medical University, Weifang 261053, Shandong Province, China
  • Supported by:
    the National Key Research & Development Program of China, No. 2018YFC2002303 (to XYZ); Project of Shanghai Science and Technology Commission, No. 19441908700 (to WJW); Two-Hundred Talents Program for Shanghai Jiao Tong University School of Medicine, No. 20152224 (to WJW); Clinical Research Project of Multidisciplinary Team of Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University, No. 201914 (to WJW)

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摘要：

文题释义：
脑卒中：是一种急性脑血管疾病，是由于脑部血管突然破裂或因血管阻塞导致血液不能流入大脑而引起脑组织损伤的一组疾病，包括缺血性和出血性卒中。脑卒中患者的步态异常主要是由于感觉运动障碍，包括肌肉无力、知觉和本体感觉障碍、痉挛或低张力。
步态分析：即研究步行规律的检查方法，旨在通过生物力学和运动学手段，揭示步态异常的关键环节及影响因素，从而指导康复评估和治疗，有助于临床诊断、疗效评估及机制研究等。

背景：脑卒中是危害中国国民健康的重大疾病之一，具有高发病率、高致残率、高死亡率及高复发率的特点。步态功能障碍或损害被认为是脑卒中最常见和最具破坏性的生理后果之一，积极的下肢康复训练可以促进步态功能的恢复，提高日常生活活动能力，改善患者的生活质量。
目的：文章旨在回顾和总结适用于脑卒中后步态量化和分析的研究成果，重点是分析最新的步态分析系统、脑卒中后步态数据处理与分析技术，以及在临床环境中的可行性和潜在价值。
方法：以“卒中、步态分析、评估、下肢、时空、运动学、动力学、足底压力、肌电图、机器学习、统计学”为中文检索词，以“stroke，gait analysis，assessment，lower limb，spatiotemporal，kinematics，kinetics，plantar pressure，Electromyography (EMG)，machine learning，statistical”为英文检索词，分别检索中国知网及PubMed数据库。检索时间范围为2000年1月至2021年12月。通过阅读文题和摘要进行初步筛选，排除中英文文献重复性研究、低质量期刊及内容不相关的文献，最终纳入60篇文献进行综述。
结果与结论：①传统的定性步态分析主要基于观察步态，具有主观性，在很大程度上受观察者经验的影响，而仪器化步态分析提供了测量的参数，具有良好的准确性和重复性，可用于整个康复过程中的诊断和评估。②快速崛起的智能可穿戴技术和人工智能，正日益引起步态研究者的更多关注：虽然它们在临床环境中的使用还没有得到很好的利用，但这些工具有望改变卒中步态量化的现状，因为它们提供了获取、存储和分析多因素复杂步态数据的手段，同时捕获了其非线性动态可变性，并提供了预测分析的宝贵好处。③在步态分析中，常通过一些特殊参数来描述步态正常与否，包括时空、运动学、动力学及肌电图参数等。了解与步行功能相关的因素可以帮助临床医生和研究人员确定在评估步行功能时应重点关注的步态相关参数。④由于常规统计方法已不能逐渐满足处理仪器化步态分析产生的具有高异质性高复杂性的大数据量，并且步态分析涉及大量相互依赖的参数，由于大量的数据及其相互关系，这些参数很难解释，为了简化评估，将机器学习应用在脑卒中后下肢步态分析中是一个很有前途的解决方案。

https://orcid.org/0000-0002-5874-8156(于文强)；https://orcid.org/0000-0003-1411-057X(王金武)

中国组织工程研究杂志出版内容重点：组织构建；骨细胞；软骨细胞；细胞培养；成纤维细胞；血管内皮细胞；骨质疏松；组织工程

关键词: 脑卒中, 步态分析, 步态参数, 统计方法, 机器学习, 康复, 诊断, 评估

Abstract: BACKGROUND: Stroke is one of the major diseases endangering the health of Chinese citizens, characterized by high morbidity, disability, mortality and recurrence. Gait dysfunction or impairment is considered to be one of the most common and devastating physiological consequences of stroke. Active lower extremity rehabilitation can promote the recovery of gait function, improve the ability to perform activities of daily living, and improve the quality of life of patients.
OBJECTIVE: To review and summarize research findings applicable to post-stroke gait quantification and analysis, with a focus on the latest gait analysis system technologies, post-stroke gait data processing and analysis techniques, and their feasibility and potential value in the clinical setting.
METHODS: Search terms included “stroke, gait analysis, assessment, lower limb, spatiotemporal, kinematics, kinetics, plantar pressure, electromyography (EMG), machine learning, statistics” in Chinese and English. CNKI and PubMed databases were searched for relevant articles published from January 2000 to December 2021. A few of classical distant documents were also included. The initial screening was performed by reading the titles and abstracts of the articles. Duplicate studies, low-quality journals and irrelevant documents were excluded and 62 articles were finally included for review.
RESULTS AND CONCLUSION: Traditional qualitative gait analysis is primarily based on observational gait and is therefore subjective and largely influenced by the observer’s experience. Quantitative gait analysis, on the other hand, provides measured parameters with good accuracy and repeatability for diagnostic and comparative assessment throughout the rehabilitation process. The rapid development of smart wearable technologies and artificial intelligence is increasingly drawing more attention to gait research. Although their use in clinical settings is not yet well exploited, these tools are expected to change the status quo in stroke gait quantification by providing the means to acquire, store, and analyze multifactorial complex gait data while capturing its nonlinear dynamic variability and providing the valuable benefit of predictive analysis. Gait analysis is often characterized by a number of specific parameters that describe normal or abnormal gait, including temporal, kinematic, kinetic, and electromyographic parameters. Understanding the factors associated with walking function can help clinicians and researchers identify gait-related parameters that should focus on when assessing walking function. As conventional statistical methods are no longer adequate to handle the large data volumes with high heterogeneity and complexity generated by instrumented gait analysis, and gait analysis involves a large number of interdependent parameters that are difficult to interpret due to the large amount of data and their interrelationships. To simplify the evaluation, the application of machine learning to gait analysis is a promising solution.  

Key words: stroke, gait analysis, gait parameter, statistics, machine learning, rehabilitation, diagnosis, assessment

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