Abstract: This review explores the potential of incorporating Machine Learning (ML) and Artificial Intelligence (AI) into the control systems of Lower Limb Rehabilitation Exoskeletons (LLREs), with a focus on its capacity to advance the field of rehabilitation robotics. It examines applications in optimizing personalized control supporting trajectory adaptation and key metrics in rehabilitation and discusses the challenges and limitations of traditional ML control methods supervised, unsupervised, and Reinforcement Learning (RL) in LLRE control. We analyze the specific aspects of traditional ML techniques in controlling the rehabilitation exoskeleton. Finally, the results of scientific solutions and developments in the implementation of gait parameters for personalized control that support the trajectory adaptation of the rehabilitation process are studied. Clinical applications and case studies directly describe the shortcomings and advantages of the field.

Key words: Lower limb rehabilitation exoskeleton')">Lower limb rehabilitation exoskeleton, Machine learning, Personalized control, Trajectory adaptation, Gait parameters