Background. The purpose of this study is to review the current literature on knee joint biomechanical gait data analysis for knee pathology classification. The review is prefaced by a presentation of the prerequisite knee joint biomechanics background and a description of biomechanical gait pattern recognition as a diagnostic tool. It is postfaced by discussions that highlight the current research findings and future directions. Methods. The review is based on a literature search in PubMed, IEEE Xplore, Science Direct, and Google Scholar on April 2019. Inclusion criteria admitted articles, written in either English or French, on knee joint biomechanical gait data classification in general. We recorded the relevant information pertaining to the investigated knee joint pathologies, the participants’ attributes, data acquisition, feature extraction, and selection used to represent the data, as well as the classification algorithms and validation of the results. Results. Thirty-one studies met the inclusion criteria for review. Conclusions. The review reveals that the importance of medical applications of knee joint biomechanical gait data classification and recent progress in data acquisition technology are fostering intense interest in the subject and giving a strong impetus to research. The review also reveals that biomechanical data during locomotion carry essential information on knee joint conditions to infer an early diagnosis. This survey paper can serve as a useful informative reference for research on the subject.

中文翻译：

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膝关节病理学评估的膝关节生物力学步态数据分类：文献综述。

背景。本研究的目的是回顾目前关于膝关节生物力学步态数据分析膝关节病理学分类的文献。这篇综述首先介绍了膝关节生物力学的先决条件，并描述了作为诊断工具的生物力学步态模式识别。它的后面是强调当前研究结果和未来方向的讨论。方法. 该评论基于 2019 年 4 月在 PubMed、IEEE Xplore、Science Direct 和 Google Scholar 中的文献搜索。纳入标准允许以英语或法语撰写的关于膝关节生物力学步态数据分类的文章。我们记录了与调查的膝关节病理、参与者的属性、数据采集、特征提取和用于表示数据的选择有关的相关信息，以及分类算法和结果的验证。结果。31 项研究符合审查的纳入标准。结论. 回顾表明，膝关节生物力学步态数据分类的医学应用的重要性和数据采集技术的最新进展正在激发对该主题的浓厚兴趣，并为研究提供了强大的动力。审查还揭示了运动过程中的生物力学数据携带有关膝关节状况的基本信息，以推断早期诊断。该调查论文可以作为有关该主题的研究的有用信息参考。