| A Short Review of Automatic Control for Lower Limb Exoskeleton |
| Paper ID : 1023-ICEEM2025 (R1) |
| Authors |
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Eman Moustafa Osman *, Alaa Khalifa, Amged Sayed A.Mahmoud, Mohamed Esmail Karar Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering, Menoufia University Menouf 32952, Egypt |
| Abstract |
| Lower limb exoskeletons present a transformative technology in rehabilitation medicine, mobility assistance, and performance enhancement. However, their widespread adoption faces significant challenges related to control systems, user intent recognition, and adaptability across diverse environments and user populations. This review examines the evolution of control strategies for lower limb exoskeletons with particular emphasis on artificial intelligence implementations. We systematically analyze the limitations of conventional control methodologies and highlight how machine learning, deep learning, and adaptive algorithms address these shortcomings through improved adaptability and personalization. Also, we assess emerging trends in artificial intelligence (AI) applications for exoskeleton control, which facilitates greater transparency and safety in human-machine interaction, enabling more robust performance across varying conditions. However, there are still limitations to creating intuitive generalized systems capable of satisfying all movement needs of patients. This review provides a guide for researchers and engineers working toward the next generation of intelligent control of lower limb exoskeletons that seamlessly integrate with human movement and intention. |
| Keywords |
| Lower limb exoskeleton, artificial intelligence, rehabilitation robotics, machine learning, control methods, human-robot interaction |
| Status: Accepted |