Isometric muscle contraction induced by repetitive peripheral magnetic stimulation (RPMS)—Modeling and identification

Bernhardt, Michael; Angerer, Bernhard; Buss, Martin; Strüppler, A.

doi:10.1016/j.bspc.2007.07.007

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…In addition to serving as a tool to design and test new neuromuscular stimulation equipment in silico , the model can replace the usually predefined sigmoid functions for the recruiting curve or the behavior of another contractile element in biomechanical models [ 44 ], [ 65 ], [ 66 ]. The incorporation into such a framework furthermore provides all temporal aspects of force generation, such as force onset, fatigue and pulse repetition rate, and enables the incorporation into a control loop for functional magnetic stimulation [ 67 ], [ 68 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Force Recruitment of Neuromuscular Magnetic Stimulation From 3D Field Model of the Thigh

Goetz

Kammermann

Helling

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Neuromuscular magnetic stimulation is a promising tool in neurorehabilitation due to its deeper penetration, notably lower distress, and respectable force levels compared to surface electrical stimulation. However, this method faces great challenges from a technological perspective. The systematic design of better equipment and the incorporation into modern training setups requires better understanding of the mechanisms and predictive quantitative models of the recruited forces. This article proposes a model for simulating the force recruitment in isometric muscle stimulation of the thigh extensors based on previous theoretical and experimental findings. The model couples a 3D field model for the physics with a parametric recruitment model. This parametric recruitment model is identified with a mixed-effects design to learn the most likely model based on available experimental data with a wide range of field conditions. This approach intentionally keeps the model as mathematically simple and statistically parsimonious as possible in order to avoid over-fitting. The work demonstrates that the force recruitment particularly depends on the effective, i.e., fiber-related cross section of the muscles, and that the local median electric field threshold amounts to about 65 V/m, which agrees well with values for magnetic stimulation in the brain. The coupled model is able to accurately predict key phenomena observed so far, such as a threshold shift for different distances between coil and body, the different recruiting performance of various coils with available measurement data in the literature, and the saturation behavior with its onset amplitude. The presented recruitment model could also be readily incorporated into dynamic models for biomechanics as soon as sufficient experimental data are available for calibration.

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Section: Discussionmentioning

confidence: 99%

Prediction of Force Recruitment of Neuromuscular Magnetic Stimulation From 3D Field Model of the Thigh

Goetz

Kammermann

Helling

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…In addition to serving as a tool to design and test new neuromuscular stimulation equipment in silico , the model can replace the usually predefined sigmoid functions for the recruiting curve or the behavior of another contractile element in biomechanical models (44, 67, 68). The incorporation into such a framework furthermore provides all temporal aspects of force generation, such as force onset, fatigue and pulse repetition rate, and enables the incorporation into a control loop for functional magnetic stimulation (69, 70).…”

Section: Discussionmentioning

confidence: 99%

Force Recruitment of Neuromuscular Magnetic Stimulation Predicted Through High-Resolution Anatomical 3D Models

Goetz

Kammermann

Helling

et al. 2021

Preprint

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Neuromuscular magnetic stimulation is a promising tool in neurorehabilitation due to its deeper penetration, notably lower distress, and respectable force levels compared to electrical stimulation. However, this method faces great challenges from a technological perspective. The systematic design of better equipment and the incorporation into modern training setups requires better understanding of the mechanisms and predictive quantitative models of the recruited forces. This article proposes a model for simulating the force recruitment in isometric muscle stimulation of the thigh extensors based on previous theoretical and experimental findings. The model couples a 3D field model for the physics with a parametric recruitment model, which is identified with a mixed-effects design to learn the most likely model based on available experimental data with a wide range of field conditions. This approach intentionally keeps the model as mathematically simple and statistically parsimonious as possible in order to avoid over-fitting. The coupled model is able to accurately predict key phenomena observed so far, such as a threshold shift for different distances between coil and body, the different recruiting performance of various coils with available measurement data in the literature, and the saturation behaviour with its onset amplitude. The presented recruitment model could also be readily incorporated into dynamic models for biomechanics as soon as sufficient experimental data are available for calibration.

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“…Es ist somit eine Online-Anwendung möglich. Das verwendete Parameteradaptionsverfahren ist in [13] erklärt.…”

Section: Identifikation Bei Nicht Messbarem Y Nunclassified

“…Für die Modellgleichung vonτ s (ausführlich in [13]) wird die Rekrutierungscharakteristik ρ(u) mit der Funktionρ(u) = ρ(arctan(u),η) mitη T = [û thrûsat ] approximiert. Das qualitative Vorwissen über die sigmoide Form mit Schwellwert u thr und Sättigungswert u sat wird so in die Identifikation eingebracht.…”

Section: Modellgleichungunclassified

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Spastikquantifizierung während therapeutischer Muskelstimulation Spasticity Quantification during Therapeutic Muscle Stimulation

Bernhardt¹,

Meißner

Strüppler

et al. 2008

At - Automatisierungstechnik

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Die repetitive periphere Magnetstimulation (RPMS) ist eine innovative Methode zur Rehabilitation von spastischer Parese nach Schlaganfall. Die Quantifizierung des Spastizitätslevels ist für die Therapie als auch für die Spastikforschung von Bedeutung. Im vorliegenden Artikel wird eine Methode zur Spastikquantifizierung basierend auf Modellbildung und Parameteridentifikation vorgestellt. Sie kann während der Stimulationstherapie eingesetzt werden und wird in einer Pilotstudie evaluiert.The repetitive peripheral magnetic stimulation (RPMS) is an innovative approach in treatment of spastic paresis, e. g. after stroke. Spasticity quantification is of importance, for therapy as well as for research on spasticity. In this article, a method based on modeling and parameter identification is presented, that allows spasticity quantification during stimulation therapy. It is tested and evaluated in a pilot study with patients.

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Isometric muscle contraction induced by repetitive peripheral magnetic stimulation (RPMS)—Modeling and identification

Cited by 9 publications

References 14 publications

Prediction of Force Recruitment of Neuromuscular Magnetic Stimulation From 3D Field Model of the Thigh

Prediction of Force Recruitment of Neuromuscular Magnetic Stimulation From 3D Field Model of the Thigh

Force Recruitment of Neuromuscular Magnetic Stimulation Predicted Through High-Resolution Anatomical 3D Models

Spastikquantifizierung während therapeutischer Muskelstimulation Spasticity Quantification during Therapeutic Muscle Stimulation

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