Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation.
This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by uti...
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doaj-78af699fb1884d14ade22757940e45c42021-03-03T23:25:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0184e6288810.1371/journal.pone.0062888Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation.Muhammad RehanKeum-Shik HongThis paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23638163/pdf/?tool=EBI |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Muhammad Rehan Keum-Shik Hong |
spellingShingle |
Muhammad Rehan Keum-Shik Hong Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. PLoS ONE |
author_facet |
Muhammad Rehan Keum-Shik Hong |
author_sort |
Muhammad Rehan |
title |
Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. |
title_short |
Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. |
title_full |
Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. |
title_fullStr |
Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. |
title_full_unstemmed |
Modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. |
title_sort |
modeling and automatic feedback control of tremor: adaptive estimation of deep brain stimulation. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations. |
url |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23638163/pdf/?tool=EBI |
work_keys_str_mv |
AT muhammadrehan modelingandautomaticfeedbackcontroloftremoradaptiveestimationofdeepbrainstimulation AT keumshikhong modelingandautomaticfeedbackcontroloftremoradaptiveestimationofdeepbrainstimulation |
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