AI in therapeutic and assistive exoskeletons and exosuits
Influences on performance and autonomy
Herman Van Der Kooij (University of Twente, TU Delft - Biomechatronics & Human-Machine Control)
Edwin Van Asseldonk (University of Twente)
Massimo Sartori (University of Twente)
Chiara Basla (ETH Zürich)
Adrian Esser (ETH Zürich)
Robert Riener (ETH Zürich, Universitat Zurich)
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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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File under embargo until 30-01-2026