Exoskeleton Control

Exoskeleton control research aims to develop robust and adaptable systems that seamlessly integrate with human movement, primarily for rehabilitation and assistance. Current efforts focus on improving control algorithms, including those based on deep learning (e.g., using neural networks and reinforcement learning) and advanced sensor integration (e.g., force myography and inertial measurement units) to personalize assistance and enhance haptic feedback. These advancements are crucial for creating more effective and comfortable exoskeletons, impacting fields like rehabilitation by providing tailored gait training and improving the quality of life for individuals with mobility impairments.