Jiao Yiran, Hart Rylea, Reading Stacey, Zhang Yanxin
Department of Exercise Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.
Department of Exercise Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.
Gait Posture. 2024 Mar;109:259-270. doi: 10.1016/j.gaitpost.2024.02.011. Epub 2024 Feb 15.
Gait classification is a clinically helpful task performed after a stroke in order to guide rehabilitation therapy. Gait disorders are commonly identified using observational gait analysis in clinical settings, but this approach is limited due to low reliability and accuracy. Data-driven gait classification can quantify gait deviations and categorise gait patterns automatically possibly improving reliability and accuracy; however, the development and clinical utility of current data driven systems has not been reviewed previously.
The purpose of this systematic review is to evaluate the literature surrounding the methodology used to develop automatic gait classification systems, and their potential effectiveness in the clinical management of stroke-affected gait.
The database search included PubMed, IEEE Xplore, and Scopus. Twenty-one studies were identified through inclusion and exclusion criteria from 407 available studies published between 2015 and 2022. Development methodology, classification performance, and clinical utility information were extracted for review.
Most of gait classification systems reported a classification accuracy between 80%-100%. However, collated studies presented methodological errors in machine learning (ML) model development. Further, many studies neglected model components such as clinical utility (e.g., predictions don't assist clinicians or therapists in making decisions, interpretability, and generalisability). We provided recommendations to guide development of future post-stroke automatic gait classification systems to better assist clinicians and therapists. Future automatic gait classification systems should emphasise the clinical significance and adopt a standardised development methodology of ML model.
步态分类是中风后进行的一项对临床有帮助的任务，用于指导康复治疗。在临床环境中，步态障碍通常通过观察性步态分析来识别，但由于可靠性和准确性较低，这种方法存在局限性。数据驱动的步态分类可以量化步态偏差并自动对步态模式进行分类，可能会提高可靠性和准确性；然而，目前数据驱动系统的开发和临床实用性此前尚未得到综述。
本系统综述的目的是评估围绕用于开发自动步态分类系统的方法的文献，以及它们在中风后步态临床管理中的潜在有效性。
数据库搜索包括PubMed、IEEE Xplore和Scopus。通过纳入和排除标准，从2015年至2022年发表的407项可用研究中确定了21项研究。提取开发方法、分类性能和临床实用性信息进行综述。
大多数步态分类系统报告的分类准确率在80%-100%之间。然而，整理后的研究在机器学习（ML）模型开发中存在方法学错误。此外，许多研究忽略了模型组件，如临床实用性（例如，预测无助于临床医生或治疗师做出决策、可解释性和可推广性）。我们提供了建议，以指导未来中风后自动步态分类系统的开发，更好地协助临床医生和治疗师。未来的自动步态分类系统应强调临床意义，并采用ML模型的标准化开发方法。
