  Accurate and rapid detection of gait phases is of utmost importance in
achieving optimal performance of powered lower-limb prostheses and
exoskeletons. With the increasing versatility and complexity of these robotic
systems, there is a growing need to enhance the performance of gait detection
algorithms. The development of reliable and functional gait detection
algorithms holds the potential to enhance precision, stability, and safety in
prosthetic devices and other rehabilitation technologies. In this systematic
review, we delve into the extensive body of research and development in the
domain of gait event detection methods, with a specific focus on their
application to prosthetic devices. Our review critically assesses various
proposed methods, aiming to identify the most effective approaches for gait
phase classification in lower limb robotic systems. Through a comprehensive
comparative analysis, we highlight the strengths and weaknesses of different
algorithms, shedding light on their performance characteristics, applicability,
and potential for further improvements. This comprehensive review was conducted
by screening two databases, namely IEEE and Scopus. The search was limited to
204 papers published from 2010 to 2023. A total of 6 papers that focused on
Heuristic, Thresholding, and Amplitude Zero Crossing involved techniques were
identified and included in the review. 33.3% of implemented Algorithms used
kinematic parameters such as joint angles, joint linear and angular velocity,
and joint angular acceleration. This study purely focuses on threshold-based
algorithms and thus paper focusing on other gait phase detection methods were
excluded.
