Sensing and assist-as-needed control in lower-limb rehabilitation exoskeletons

Abstract

Lower-limb rehabilitation exoskeletons are often discussed in terms of mechanics, sensing, and control, yet their rehabilitation value depends on how these elements work together during human–robot interaction. This review focuses on the integration of sensing, compliant actuation, and assist-as-needed control in lower-limb rehabilitation exoskeletons. Recent research suggests that effective assistance depends not only on actuator output, but also on reliable gait-state detection, intention-related sensing, mechanical transparency, and real-time adaptation. Current progress in sensing based on inertial measurement units (IMUs), force and pressure measurements, and electromyography (EMG) is reviewed, followed by discussion of how actuation choice and mechanical compliance influence safe and effective assistance. Major control strategies, including trajectory tracking, impedance control, hierarchical control, learning-based methods, and assist-as-needed approaches, are then compared. Remaining barriers to clinical translation include signal instability, safety and certification requirements, and the persistent gap between laboratory performance and patient-specific rehabilitation needs. Future progress will likely depend on tighter co-design of sensing, hardware compliance, and cooperative control.