Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study.

ABSTRACT: (1) Background: An iterative learning control (ILC) strategy was developed for a "Muscle First" Motor-Assisted Hybrid Neuroprosthesis (MAHNP). The MAHNP combines a backdrivable exoskeletal brace with neural stimulation technology to enable persons with paraplegia due to spinal cord injury (SCI) to execute ambulatory motions and walk upright. (2) Methods: The ILC strategy was developed to swing the legs in a biologically inspired ballistic fashion. It maximizes muscular recruitment and activates the motorized exoskeletal bracing to assist the motion as needed. The control algorithm was tested using an anatomically realistic three-dimensional musculoskeletal model of the lower leg and pelvis suitably modified to account for exoskeletal inertia. The model was developed and tested with the Ope

SUBMITTER: Makowski NS 

PROVIDER: S-EPMC8869465 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature