AI in therapeutic and assistive exoskeletons and exosuits
Influences on performance and autonomy
Herman van der Kooij (TU Delft - Biomechatronics & Human-Machine Control, University of Twente)
Edwin H.F. van Asseldonk (University of Twente)
Massimo Sartori (University of Twente)
Chiara Basla (ETH Zürich)
Adrian Esser (ETH Zürich)
Robert Riener (Universitat Zurich, ETH Zürich)
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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.