Anterior cruciate ligament (ACL) injuries are prevalent in sports and daily life, often leading to functional instability and long-term complications such as osteoarthritis. This literature review synthesizes advancements in postoperative gait analysis following ACL reconstruction (ACLR), focusing on biomechanical alterations, rehabilitation outcomes, and emerging technologies. Current methodologies, including three-dimensional motion capture, force plate kinetics, surface electromyography (sEMG), wearable sensors, machine learning and artificial intelligence, reveal persistent kinematic asymmetries, and altered joint loading patterns in ACLR patients. Rehabilitation interventions, such as neuromuscular training, biofeedback, and AI-assisted systems, show promise in restoring dynamic stability but require standardization and cost optimization. Limitations of existing studies include small sample sizes, short follow-up periods, and methodological inconsistencies. Future research should prioritize multicenter longitudinal studies, multimodal data integration, and AI-driven precision rehabilitation to optimize recovery and mitigate long-term risks. This study aims to elucidate the role of gait analysis in optimizing rehabilitation protocols and mitigating long-term complications by evaluating the strengths and limitations of existing approaches.
Yu et al. (Tue,) studied this question.