Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

<p>Abstract</p> <p>Background</p> <p>The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour w...

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Main Authors: De Momi Elena, Ferrante Simona, Pedrocchi Alessandra, Ferrigno Giancarlo
Format: Article
Language:English
Published: BMC 2006-10-01
Series:Journal of NeuroEngineering and Rehabilitation
Online Access:http://www.jneuroengrehab.com/content/3/1/25
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spelling doaj-740c0345b4b1442b8ccdde8d25d15a382020-11-25T01:58:20ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032006-10-01312510.1186/1743-0003-3-25Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networksDe Momi ElenaFerrante SimonaPedrocchi AlessandraFerrigno Giancarlo<p>Abstract</p> <p>Background</p> <p>The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required.</p> <p>Methods</p> <p>The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out.</p> <p>Results</p> <p>The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances.</p> <p>Conclusion</p> <p>Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice.</p> http://www.jneuroengrehab.com/content/3/1/25
collection DOAJ
language English
format Article
sources DOAJ
author De Momi Elena
Ferrante Simona
Pedrocchi Alessandra
Ferrigno Giancarlo
spellingShingle De Momi Elena
Ferrante Simona
Pedrocchi Alessandra
Ferrigno Giancarlo
Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks
Journal of NeuroEngineering and Rehabilitation
author_facet De Momi Elena
Ferrante Simona
Pedrocchi Alessandra
Ferrigno Giancarlo
author_sort De Momi Elena
title Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks
title_short Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks
title_full Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks
title_fullStr Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks
title_full_unstemmed Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks
title_sort error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks
publisher BMC
series Journal of NeuroEngineering and Rehabilitation
issn 1743-0003
publishDate 2006-10-01
description <p>Abstract</p> <p>Background</p> <p>The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required.</p> <p>Methods</p> <p>The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out.</p> <p>Results</p> <p>The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances.</p> <p>Conclusion</p> <p>Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice.</p>
url http://www.jneuroengrehab.com/content/3/1/25
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AT ferrantesimona errormappingcontrolleraclosedloopneuroprosthesiscontrolledbyartificialneuralnetworks
AT pedrocchialessandra errormappingcontrolleraclosedloopneuroprosthesiscontrolledbyartificialneuralnetworks
AT ferrignogiancarlo errormappingcontrolleraclosedloopneuroprosthesiscontrolledbyartificialneuralnetworks
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