Technological advances that involve computing and artificial intelligence (AI) have led to advances in analysis methods. Fuzzy logic (FL) serves as a qualitative interpretation tool for AI. The objective of this systematic review is to investigate the methods of human movement (HM) analysis using AI through FL to understand the characteristics of the movement of healthy people. To identify relevant studies published up to April 19, 2019, we conducted a study of the PubMed, Scopus, ScienceDirect, and IEEE Xplore databases. We included studies that evaluated HM through AI using FL in healthy people. A total of 951 articles were examined, of which six were selected because they met the criteria presented in the methods. The protocols had high heterogeneity, yet all articles selected presented statistically satisfactory results, in addition to low errors or a false positive index. Only one selected article presented protocol applicability within the free-living model. Generally, AI using FL is a good tool to help assess HM in healthy people, but the model still needs new data acquisition entries to make it applicability within the free-living model.

中文翻译：

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基于模糊逻辑的人工智能在健康人人体运动分析中的系统评价

涉及计算和人工智能 (AI) 的技术进步导致了分析方法的进步。模糊逻辑 (FL) 是 AI 的定性解释工具。本系统评价的目的是通过 FL 研究使用 AI 的人体运动 (HM) 分析方法，以了解健康人的运动特征。为了确定截至 2019 年 4 月 19 日发表的相关研究，我们对 PubMed、Scopus、ScienceDirect 和 IEEE Xplore 数据库进行了研究。我们纳入了使用 FL 在健康人群中通过 AI 评估 HM 的研究。共检查了 951 篇文章，其中 6 篇被选中，因为它们符合方法中提出的标准。方案具有高度异质性，但所有选择的文章都呈现出令人满意的结果 除了低错误或误报指数。只有一篇选定的文章介绍了自由生活模型中的协议适用性。通常，使用 FL 的 AI 是帮助评估健康人 HM 的好工具，但该模型仍需要新的数据采集条目以使其适用于自由生活模型。