Trajectory Optimization and Model Predictive Control for Functional Electrical Stimulation-Controlled Reaching

Functional electrical stimulation (FES) offers promise as a technology to restore reaching motions to individuals with spinal cord injuries. To date, the level of reaching necessary for everyday use has not been achieved due to the complexity and limitations of the arm and muscles of an individual w...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 7; H. 2; S. 3093 - 3098
Hauptverfasser: Wolf, Derek N., Schearer, Eric M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computational modeling
Data models
Manipulators
Mathematical models
Model learning for control
motion and path planning
motion control
Muscles
Predictive control
prosthetics and exoskeletons
rehabilitation robotics
Shoulder
Spinal cord injuries
Stimulation
Trajectory optimization
ISSN:2377-3766, 2377-3766
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Abstract Functional electrical stimulation (FES) offers promise as a technology to restore reaching motions to individuals with spinal cord injuries. To date, the level of reaching necessary for everyday use has not been achieved due to the complexity and limitations of the arm and muscles of an individual with a spinal cord injury. To improve the performance of FES-driven reaching controllers, we developed a trajectory optimization and model predictive control scheme that incorporates knowledge of the person-specific muscle capabilities and arm dynamics. Our controller achieved 3D reaching motions with an average accuracy of 8.5 cm and demonstrated an ability to reach targets throughout the workspace. With improvements to the model, this control scheme has the potential to unlock many daily reaching tasks for individuals with spinal cord injuries.
AbstractList Functional electrical stimulation (FES) offers promise as a technology to restore reaching motions to individuals with spinal cord injuries. To date, the level of reaching necessary for everyday use has not been achieved due to the complexity and limitations of the arm and muscles of an individual with a spinal cord injury. To improve the performance of FES-driven reaching controllers, we developed a trajectory optimization and model predictive control scheme that incorporates knowledge of the person-specific muscle capabilities and arm dynamics. Our controller achieved 3D reaching motions with an average accuracy of 8.5 cm and demonstrated an ability to reach targets throughout the workspace. With improvements to the model, this control scheme has the potential to unlock many daily reaching tasks for individuals with spinal cord injuries.
Author Wolf, Derek N.
Schearer, Eric M.
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  surname: Wolf
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  organization: Department of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA
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  organization: Center for Human-Machine Systems, Cleveland State University, the Cleveland Functional Electrical Stimulation Center, and the Department of Physical Medicine and Rehabilitation, MetroHealth Medical Center, Cleveland, OH, USA
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Snippet Functional electrical stimulation (FES) offers promise as a technology to restore reaching motions to individuals with spinal cord injuries. To date, the level...
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SubjectTerms Computational modeling
Data models
Manipulators
Mathematical models
Model learning for control
motion and path planning
motion control
Muscles
Predictive control
prosthetics and exoskeletons
rehabilitation robotics
Shoulder
Spinal cord injuries
Stimulation
Trajectory optimization
Title Trajectory Optimization and Model Predictive Control for Functional Electrical Stimulation-Controlled Reaching
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