A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces.

Journal: Computers in biology and medicine
PMID: 38925086

Abstract

Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person's movements-offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user's residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations.

Authors

  • Thomas E Augenstein
    Robotics Department, University of Michigan, Ann Arbor, MI, USA; NeuRRo Lab, Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA.
  • Deepak Nagalla
    Robotics Department, University of Michigan, Ann Arbor, MI, USA; NeuRRo Lab, Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA.
  • Alexander Mohacey
    Robotics Department, University of Michigan, Ann Arbor, MI, USA; NeuRRo Lab, Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA.
  • Luis H Cubillos
    Robotics Department, University of Michigan, Ann Arbor, MI, USA; NeuRRo Lab, Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA.
  • Mei-Hua Lee
    Department of Kinesiology, Michigan State University, East Lansing, Michigan, United States of America.
  • Rajiv Ranganathan
    Department of Mechanical Engineering, Michigan State University, East Lansing, Michigan, United States of America.
  • Chandramouli Krishnan
    Department of Physical Medicine and Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: mouli@umich.edu.

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