Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding
Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complex...
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Published in: | Frontiers in neurorobotics Vol. 16; p. 918001 |
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Main Authors: | , , , , , , , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Lausanne
Frontiers Research Foundation
28-06-2022
Frontiers Media S.A |
Subjects: | |
Online Access: | Get full text |
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Summary: | Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom (DOF) across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two DOFs on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 DOFs for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control
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a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: John E. Downey, The University of Chicago, United States; Solaiman Shokur, Swiss Federal Institute of Technology Lausanne, Switzerland Edited by: Giacomo Valle, ETH Zürich, Switzerland Present address: Tessy M. Thomas, The University of Texas Health Science Center at Houston, Houston, TX, United States |
ISSN: | 1662-5218 1662-5218 |
DOI: | 10.3389/fnbot.2022.918001 |