Deep Learning Approaches for Enhanced Lower-Limb Exoskeleton Control: A Review

Abstract

Recent advancements in robotics have pushed the development of active exoskeletons and orthoses for assistive, augmentative, and rehabilitative purposes. Deep Learning approaches, particularly in motion analysis and prediction, hold promise for enhanced control of lower limb exoskeletons, offering the potential for improved outcomes in assistive and rehabilitative interventions. This review paper explores recent advancements in deep learning approaches for controlling lower limb exoskeletons. The study encompasses papers published from 2018 to the present, focusing on various aspects of deep learning models, including recognition, prediction, and other related tasks. The paper includes 103 papers covering various tasks and parameters like the gait phase, kinematics, and kinetics of the lower limb exoskeleton. Each aspect is thoroughly examined, highlighting the parameters utilized in the respective models. Moreover, the results obtained from these approaches are evaluated and compared against classical control strategies, shedding light on their effectiveness and potential benefits. The review also addresses the limitations of current deep learning techniques in lower limb exoskeleton control and outlines potential avenues for future research and improvement. By consolidating the latest findings and advancements in this field, this review paper provides valuable insights into the application of deep learning in the control of lower limb exoskeletons, paving the way for enhanced rehabilitation and assistive technologies in the future.

Keywords

• Deep learning
• exoskeletons
• rehabilitation
• neural networks