Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance

Functional Electrical Stimulation (FES) can be an effective tool to augment paretic muscle function and restore normal ankle function. Our approach incorporates a real-time, data-driven Model Predictive Control (MPC) scheme built upon a Koopman operator theory (KOT) framework. This framework adeptly...

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Main Authors: Mayank Singh, Noor Hakam, Trisha M. Kesar, Nitin Sharma
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10929647/
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author Mayank Singh
Noor Hakam
Trisha M. Kesar
Nitin Sharma
author_facet Mayank Singh
Noor Hakam
Trisha M. Kesar
Nitin Sharma
author_sort Mayank Singh
collection DOAJ
description Functional Electrical Stimulation (FES) can be an effective tool to augment paretic muscle function and restore normal ankle function. Our approach incorporates a real-time, data-driven Model Predictive Control (MPC) scheme built upon a Koopman operator theory (KOT) framework. This framework adeptly captures the complex nonlinear dynamics of ankle motion in a linearized form, enabling the application of linear control approaches for highly nonlinear FES-actuated dynamics. Our method accurately predicts the FES-induced ankle movements, accounting for nonlinear muscle actuation dynamics, including the muscle activation for both plantarflexors and dorsiflexors (Tibialis Anterior (TA)). The linear prediction model derived through KOT allowed the formulation of the MPC problem with linear state space dynamics, enhancing the FES-driven control’s real-time feasibility, precision, and adaptability. We demonstrate the effectiveness and applicability of our approach through comprehensive simulations and experimental trials, including three participants with no disability and a participant with Multiple Sclerosis. Our findings highlight the potential of a KOT-based MPC approach for FES-based gait assistance that offers effective and personalized assistance for individuals with gait impairment conditions.
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spelling doaj-art-7aee9d993a8245fe9d82916a9630c9fc2025-08-20T01:54:40ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01331252126210.1109/TNSRE.2025.355193310929647Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion AssistanceMayank Singh0https://orcid.org/0009-0001-4163-9041Noor Hakam1https://orcid.org/0009-0002-5945-9513Trisha M. Kesar2Nitin Sharma3https://orcid.org/0000-0003-1872-0156Department of Electrical Engineering, North Carolina State University, Raleigh, NC, USAUNC/NC State Lampe Joint Department of Biomedical Engineering, NC State University, Raleigh, NC, USADepartment of Rehabilitation Medicine, Division of Physical Therapy, Emory University School of Medicine, Atlanta, GA, USAUNC/NC State Lampe Joint Department of Biomedical Engineering, NC State University, Raleigh, NC, USAFunctional Electrical Stimulation (FES) can be an effective tool to augment paretic muscle function and restore normal ankle function. Our approach incorporates a real-time, data-driven Model Predictive Control (MPC) scheme built upon a Koopman operator theory (KOT) framework. This framework adeptly captures the complex nonlinear dynamics of ankle motion in a linearized form, enabling the application of linear control approaches for highly nonlinear FES-actuated dynamics. Our method accurately predicts the FES-induced ankle movements, accounting for nonlinear muscle actuation dynamics, including the muscle activation for both plantarflexors and dorsiflexors (Tibialis Anterior (TA)). The linear prediction model derived through KOT allowed the formulation of the MPC problem with linear state space dynamics, enhancing the FES-driven control’s real-time feasibility, precision, and adaptability. We demonstrate the effectiveness and applicability of our approach through comprehensive simulations and experimental trials, including three participants with no disability and a participant with Multiple Sclerosis. Our findings highlight the potential of a KOT-based MPC approach for FES-based gait assistance that offers effective and personalized assistance for individuals with gait impairment conditions.https://ieeexplore.ieee.org/document/10929647/Functional electrical stimulation (FES)extended dynamic mode decomposition (EDMD)model predictive control (MPC)gait assistancenonlinear dynamics
spellingShingle Mayank Singh
Noor Hakam
Trisha M. Kesar
Nitin Sharma
Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Functional electrical stimulation (FES)
extended dynamic mode decomposition (EDMD)
model predictive control (MPC)
gait assistance
nonlinear dynamics
title Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance
title_full Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance
title_fullStr Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance
title_full_unstemmed Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance
title_short Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance
title_sort koopman based model predictive control of functional electrical stimulation for ankle dorsiflexion and plantarflexion assistance
topic Functional electrical stimulation (FES)
extended dynamic mode decomposition (EDMD)
model predictive control (MPC)
gait assistance
nonlinear dynamics
url https://ieeexplore.ieee.org/document/10929647/
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