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Accurate and rapid detection of gait phases is of utmost importance inachieving optimal performance of powered lower-limb prostheses andexoskeletons. With the increasing versatility and complexity of these roboticsystems, there is a growing need to enhance the performance of gait detectionalgorithms. The development of reliable and functional gait detectionalgorithms holds the potential to enhance precision, stability, and safety inprosthetic devices and other rehabilitation technologies. In this systematicreview, we delve into the extensive body of research and development in thedomain of gait event detection methods, with a specific focus on theirapplication to prosthetic devices. Our review critically assesses variousproposed methods, aiming to identify the most effective approaches for gaitphase classification in lower limb robotic systems. Through a comprehensivecomparative analysis, we highlight the strengths and weaknesses of differentalgorithms, shedding light on their performance characteristics, applicability,and potential for further improvements. This comprehensive review was conductedby screening two databases, namely IEEE and Scopus. The search was limited to204 papers published from 2010 to 2023. A total of 6 papers that focused onHeuristic, Thresholding, and Amplitude Zero Crossing involved techniques wereidentified and included in the review. 33.3% of implemented Algorithms usedkinematic parameters such as joint angles, joint linear and angular velocity,and joint angular acceleration. This study purely focuses on threshold-basedalgorithms and thus paper focusing on other gait phase detection methods wereexcluded.

Evaluating Intelligent Algorithms for Gait Phase Classification in Lower Limb Robotic Systems