Robotic gait trainers have transformed rehabilitation by providing precise, consistent therapy for individuals with neurological and orthopedic impairments. Over the past two decades, advances in control strategies, sensor fusion, and human–robot interaction have significantly improved therapeutic outcomes. The integration of adaptive algorithms and machine learning has enabled real-time personalization of training, while wearable exoskeletons and bio-inspired actuation have enhanced biomechanical fidelity. Immersive technologies such as virtual reality have further enriched rehabilitation. However, challenges persist, including safety risks, cost, patient adherence, and the lack of long-term efficacy data. This review synthesizes the evolution of robotic gait trainers, highlighting contradictions in literature, unresolved limitations, and gaps in clinical translation. An integrative framework is proposed that combines hybrid assistive systems, adaptive algorithms, and multimodal feedback to directly address these shortcomings and guide future development.
Sakhardande et al. (Wed,) studied this question.