Exploring Control Approaches for Robot-Based Gait Rehabilitation and Locomotion Assistance: A Comprehensive Review

Context: Robot-assisted gait rehabilitation is a rapidly evolving field that aims to enhance therapeutic outcomes through the integration of advanced robotic systems. These systems reduce the physical burden on therapists while enabling intensive and repetitive patient training. However, current methods often require manual adjustments tailored to individual gait patterns, limiting their generalizability and efficiency. Objective: This paper provides a comprehensive review of control approaches for robot-based gait rehabilitation and locomotion assistance, focusing on improving adaptability and human-robot synchronization. The goal is to identify effective techniques and methods that optimize exoskeleton performance and improve patient outcomes. Methodology: A state-of-the-art review was conducted through a structured literature search across multiple scientific databases to identify research on lower-limb exoskeletons, centering on control strategies for robot-assisted gait rehabilitation. The search initially employed broad keywords such as “lower limb exoskeletons,” “extremity lower exoskeletons,” and “lower limb robotic exoskeletons,” among others. It was then refined using more specific terms including “lower limb exoskeleton control,” “assisted gait control,” “assistance control strategy,” “wearable robots control,” and “robotic rehabilitation,” among others. Well-defined inclusion criteria ensured thematic relevance, methodological rigor, and comprehensive coverage of recent advancements. A total of 77 studies meeting these criteria were analyzed to identify and categorize control approaches, and to discuss their scope, advantages, and limitations rather than directly assessing their clinical effectiveness or patient outcome improvements. Future research should focus on data-driven control methods to address individual variability and improve exoskeleton adaptability. Results: The review highlights a taxonomy of essential control strategies for robot-assisted gait, such as trajectory tracking based on position, force-impedance control, control driven by bio-signals, and adaptive control techniques. Real-time adaptive strategies, such as Velocity Field Control and Assist-As-Needed approaches, demonstrate strong potential for improving synchronization and reducing manual tuning requirements. Conclusions: Adaptive control strategies, particularly Assist-As-Needed controllers, are effective in encouraging active patient participation and optimizing rehabilitation outcomes. Financing: This research work received funding from Universidad del Valle under the project "Plataforma tecnológica modular para la valoración objetiva de la marcha humana" (C.I. 21259) and from the Ministry of Science, Technology, and Innovation (Minciencias - Open call 647).