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A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling

Functional electrical stimulation (FES) is a technique used in rehabilitation, allowing the recreation or facilitation of a movement or function, by electrically inducing the activation of targeted muscles. FES during cycling often uses activation patterns which are based on the crank angle of the p...

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Autores principales: Le Guillou, Ronan, Schmoll, Martin, Sijobert, Benoît, Lobato Borges, David, Fachin-Martins, Emerson, Resende, Henrique, Pissard-Gibollet, Roger, Fattal, Charles, Azevedo Coste, Christine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272114/
https://www.ncbi.nlm.nih.gov/pubmed/34283104
http://dx.doi.org/10.3390/s21134571
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author Le Guillou, Ronan
Schmoll, Martin
Sijobert, Benoît
Lobato Borges, David
Fachin-Martins, Emerson
Resende, Henrique
Pissard-Gibollet, Roger
Fattal, Charles
Azevedo Coste, Christine
author_facet Le Guillou, Ronan
Schmoll, Martin
Sijobert, Benoît
Lobato Borges, David
Fachin-Martins, Emerson
Resende, Henrique
Pissard-Gibollet, Roger
Fattal, Charles
Azevedo Coste, Christine
author_sort Le Guillou, Ronan
collection PubMed
description Functional electrical stimulation (FES) is a technique used in rehabilitation, allowing the recreation or facilitation of a movement or function, by electrically inducing the activation of targeted muscles. FES during cycling often uses activation patterns which are based on the crank angle of the pedals. Dynamic changes in their underlying predefined geometrical models (e.g., change in seating position) can lead to desynchronised contractions. Adaptive algorithms with a real-time interpretation of anatomical segments can avoid this and open new possibilities for the automatic design of stimulation patterns. However, their ability to accurately and precisely detect stimulation triggering events has to be evaluated in order to ensure their adaptability to real-case applications in various conditions. In this study, three algorithms (Hilbert, BSgonio, and Gait Cycle Index (GCI) Observer) were evaluated on passive cycling inertial data of six participants with spinal cord injury (SCI). For standardised comparison, a linear phase reference baseline was used to define target events (i.e., 10%, 40%, 60%, and 90% of the cycle’s progress). Limits of agreement (LoA) of ±10% of the cycle’s duration and Lin’s concordance correlation coefficient (CCC) were used to evaluate the accuracy and precision of the algorithm’s event detections. The delays in the detection were determined for each algorithm over 780 events. Analysis showed that the Hilbert and BSgonio algorithms validated the selected criteria (LoA: +5.17/−6.34% and +2.25/−2.51%, respectively), while the GCI Observer did not (LoA: +8.59/−27.89%). When evaluating control algorithms, it is paramount to define appropriate criteria in the context of the targeted practical application. To this end, normalising delays in event detection to the cycle’s duration enables the use of a criterion that stays invariable to changes in cadence. Lin’s CCC, comparing both linear correlation and strength of agreement between methods, also provides a reliable way of confirming comparisons between new control methods and an existing reference.
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spelling pubmed-82721142021-07-11 A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling Le Guillou, Ronan Schmoll, Martin Sijobert, Benoît Lobato Borges, David Fachin-Martins, Emerson Resende, Henrique Pissard-Gibollet, Roger Fattal, Charles Azevedo Coste, Christine Sensors (Basel) Article Functional electrical stimulation (FES) is a technique used in rehabilitation, allowing the recreation or facilitation of a movement or function, by electrically inducing the activation of targeted muscles. FES during cycling often uses activation patterns which are based on the crank angle of the pedals. Dynamic changes in their underlying predefined geometrical models (e.g., change in seating position) can lead to desynchronised contractions. Adaptive algorithms with a real-time interpretation of anatomical segments can avoid this and open new possibilities for the automatic design of stimulation patterns. However, their ability to accurately and precisely detect stimulation triggering events has to be evaluated in order to ensure their adaptability to real-case applications in various conditions. In this study, three algorithms (Hilbert, BSgonio, and Gait Cycle Index (GCI) Observer) were evaluated on passive cycling inertial data of six participants with spinal cord injury (SCI). For standardised comparison, a linear phase reference baseline was used to define target events (i.e., 10%, 40%, 60%, and 90% of the cycle’s progress). Limits of agreement (LoA) of ±10% of the cycle’s duration and Lin’s concordance correlation coefficient (CCC) were used to evaluate the accuracy and precision of the algorithm’s event detections. The delays in the detection were determined for each algorithm over 780 events. Analysis showed that the Hilbert and BSgonio algorithms validated the selected criteria (LoA: +5.17/−6.34% and +2.25/−2.51%, respectively), while the GCI Observer did not (LoA: +8.59/−27.89%). When evaluating control algorithms, it is paramount to define appropriate criteria in the context of the targeted practical application. To this end, normalising delays in event detection to the cycle’s duration enables the use of a criterion that stays invariable to changes in cadence. Lin’s CCC, comparing both linear correlation and strength of agreement between methods, also provides a reliable way of confirming comparisons between new control methods and an existing reference. MDPI 2021-07-03 /pmc/articles/PMC8272114/ /pubmed/34283104 http://dx.doi.org/10.3390/s21134571 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Le Guillou, Ronan
Schmoll, Martin
Sijobert, Benoît
Lobato Borges, David
Fachin-Martins, Emerson
Resende, Henrique
Pissard-Gibollet, Roger
Fattal, Charles
Azevedo Coste, Christine
A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling
title A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling
title_full A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling
title_fullStr A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling
title_full_unstemmed A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling
title_short A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling
title_sort novel framework for quantifying accuracy and precision of event detection algorithms in fes-cycling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272114/
https://www.ncbi.nlm.nih.gov/pubmed/34283104
http://dx.doi.org/10.3390/s21134571
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