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AI in therapeutic and assistive exoskeletons and exosuits: Influences on performance and autonomy
Herman van der Kooij1,2, Edwin H F van Asseldonk1, Massimo Sartori1
1Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands.
Abstract:
Therapeutic and assistive exoskeletons and exosuits show promise in both clinical and real-world settings. Improving their autonomy can enhance usability, effectiveness, and cost efficiency. This Review presents a generic control framework for autonomous operation of upper and lower limb devices and reviews current advancements and future directions. We highlight how data-driven machine learning aids in intention recognition, synchronization, patient assessment, and task-agnostic control. In addition, we discuss how reinforcement learning optimizes control policies through digital human twins and how generative AI supports therapy planning and patient engagement. Richer patient-specific data and more accurate digital twins are needed for clinical validation and widespread deployment.
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