Over the past few years, the division of gait phases has emerged as a complex area of research that carries significant importance for various applications in the field of gait technologies. The accurate partitioning of gait phases plays a crucial role in advancing these applications. Researchers have been exploring a range of sensors that can be employed to provide data for algorithms involved in gait phase partitioning. These sensors can be broadly categorized into two types: wearable and non-wearable, each offering unique advantages and capabilities. In our study aimed at examining the current approaches to gait analysis and detection specifically designed for implementation in ambulatory rehabilitation systems, we conducted a comprehensive meta-analysis of existing research studies. Our analysis revealed a diverse range of sensors and sensor combinations that demonstrate the ability to analyze gait patterns in ambulatory settings. These sensor options vary from basic force-based binary switches to more intricate setups incorporating multiple inertial sensors and sophisticated algorithms. The findings highlight the wide spectrum of available technologies and methodologies used in gait analysis for ambulatory applications. To conduct an extensive review, we systematically examined two prominent databases, IEEE and Scopus, with the aim of identifying relevant studies pertaining to gait analysis. The search criteria were limited to 189 papers published between 1999 and 2023. From this pool, we identified and included five papers that specifically focused on various techniques including Thresholding, Quasi-static method, adaptive classifier, and SVM-based approaches. These selected papers provided valuable insights for our review.

翻译：近年来，步态相位划分已成为一个复杂的研究领域，在步态技术的多种应用中具有重要意义。精确的步态相位划分对于推进这些应用起着关键作用。研究人员一直在探索可用于为步态相位划分算法提供数据的多种传感器。这些传感器大致可分为两类：可穿戴式和非可穿戴式，每种类型都具有独特的优势和能力。在本研究中，我们旨在考察当前专为用于移动康复系统而设计的步态分析与检测方法，为此我们对现有研究进行了全面的荟萃分析。我们的分析揭示了多种传感器及传感器组合，这些组合能够分析移动环境中的步态模式。这些传感器选项从基于力的简单二进制开关，到集成多个惯性传感器和复杂算法的更精密设置不等。研究结果凸显了用于移动应用步态分析的可利用技术与方法的广泛谱系。为进行广泛综述，我们系统检索了IEEE和Scopus两个知名数据库，旨在识别与步态分析相关的适宜研究。检索标准限于1999年至2023年间发表的189篇论文。从这些论文中，我们确定并纳入了五篇特别关注阈值法、准静态法、自适应分类器及基于SVM方法等不同技术的论文。这些入选论文为本综述提供了宝贵的见解。