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About

Yongqiang Liang is a researcher whose work sits at the intersection of robotics, neuroscience, and rehabilitation medicine, with a particular focus on developing intelligent robotic training paradigms for spinal cord injury recovery. His most influential contribution — cited over 346 times — introduced the groundbreaking "assist-as-needed" (AAN) framework, which challenged prevailing assumptions in robotic rehabilitation by demonstrating that rigid, fixed kinematic control may actually impede motor recovery. Instead, Liang and colleagues argued that preserving natural neuromuscular variability is essential to effective rehabilitative training, a finding with profound implications for how robotic devices are designed and deployed in clinical settings. Building on this foundational insight, Liang extended the AAN paradigm through preclinical studies using adult spinal mice with complete thoracic transections, systematically comparing the effects of consistent versus variable robotic trajectory control on hindlimb locomotor recovery. These studies provided critical experimental evidence supporting variability as a therapeutic asset rather than a limitation. Collectively, Liang's body of work has helped reshape the conceptual and practical framework for robot-assisted rehabilitation, influencing both biomedical engineering research and clinical approaches to spinal cord injury treatment.