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Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition
Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorith...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625080/ https://www.ncbi.nlm.nih.gov/pubmed/26578873 http://dx.doi.org/10.3389/fnins.2015.00416 |
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author | Ortiz-Catalan, Max |
author_facet | Ortiz-Catalan, Max |
author_sort | Ortiz-Catalan, Max |
collection | PubMed |
description | Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation therapies. In the present study, cardinality is introduced and compared with traditional time-domain (Hudgins' set) and other recently proposed myoelectric features (for example, rough entropy). Cardinality was found to consistently outperform other features, including those that are more sophisticated and computationally expensive, despite variations in sampling frequency, time window length, contraction dynamics, type, and number of movements (single or simultaneous), and classification algorithms. Provided that the signal resolution is kept between 12 and 14 bits, cardinality improves myoelectric pattern recognition for the prediction of motion volition. This technology is instrumental for the rehabilitation of amputees and patients with motor impairments where myoelectric signals are viable. All code and data used in this work is available online within BioPatRec. |
format | Online Article Text |
id | pubmed-4625080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-46250802015-11-17 Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition Ortiz-Catalan, Max Front Neurosci Neuroscience Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation therapies. In the present study, cardinality is introduced and compared with traditional time-domain (Hudgins' set) and other recently proposed myoelectric features (for example, rough entropy). Cardinality was found to consistently outperform other features, including those that are more sophisticated and computationally expensive, despite variations in sampling frequency, time window length, contraction dynamics, type, and number of movements (single or simultaneous), and classification algorithms. Provided that the signal resolution is kept between 12 and 14 bits, cardinality improves myoelectric pattern recognition for the prediction of motion volition. This technology is instrumental for the rehabilitation of amputees and patients with motor impairments where myoelectric signals are viable. All code and data used in this work is available online within BioPatRec. Frontiers Media S.A. 2015-10-29 /pmc/articles/PMC4625080/ /pubmed/26578873 http://dx.doi.org/10.3389/fnins.2015.00416 Text en Copyright © 2015 Ortiz-Catalan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ortiz-Catalan, Max Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition |
title | Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition |
title_full | Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition |
title_fullStr | Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition |
title_full_unstemmed | Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition |
title_short | Cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition |
title_sort | cardinality as a highly descriptive feature in myoelectric pattern recognition for decoding motor volition |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625080/ https://www.ncbi.nlm.nih.gov/pubmed/26578873 http://dx.doi.org/10.3389/fnins.2015.00416 |
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