AI in therapeutic and assistive exoskeletons and exosuits: Influences on performance and autonomy.

Journal: Science robotics
Published Date:

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.

Authors

  • Herman van der Kooij
  • Edwin H F van Asseldonk
    120691 University of Twente, Enschede, The Netherlands.
  • Massimo Sartori
  • Chiara Basla
  • Adrian Esser
    Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland.
  • Robert Riener
    Sensory-Motor Systems Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.