Classification complexity in myoelectric pattern recognition

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Fältnamn	Indikatorer	Metadata
000		04003naa a2200397 4500
001		oai:research.chalmers.se:c6be84e0-7b9f-4675-a16c-248cfd67d069
003		SwePub
008		171008s2017 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://doi.org/10.1186/s12984-017-0283-52 DOI
024	7	a https://research.chalmers.se/publication/2508922 URI
040		a (SwePub)cth
041		a engb eng
042		9 SwePub
072	7	a art2 swepub-publicationtype
072	7	a ref2 swepub-contenttype
100	1	a Nilsson, Niclasu Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)nniclas
245	1 0	a Classification complexity in myoelectric pattern recognition
264		c 2017-07-10
264	1	b Springer Science and Business Media LLC,c 2017
338		a electronic2 rdacarrier
520		a Background: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition. Methods: CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback-Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec. Results: NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers' sensitivity to such redundancy. Conclusions: This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance.
650	7	a TEKNIK OCH TEKNOLOGIERx Medicinteknik0 (SwePub)2062 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Medical Engineering0 (SwePub)2062 hsv//eng
653		a Prosthesis control
653		a Electromyography
653		a Myoelectric pattern recognition
653		a Classification complexity
700	1	a Håkansson, Bo,d 1953u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)boh
700	1	a Ortiz Catalan, Max Jair,d 1982u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)maxo
710	2	a Chalmers tekniska högskola4 org
773	0	t Journal of NeuroEngineering and Rehabilitationd : Springer Science and Business Media LLCg 14:1, s. Article no. 68 -q 14:1<Article no. 68 -x 1743-0003
856	4	u http://dx.doi.org/10.1186/s12984-017-0283-5y FULLTEXT
856	4	u https://research.chalmers.se/publication/250892/file/250892_Fulltext.pdfx primaryx freey FULLTEXT
856	4	u https://doi.org/10.1186/s12984-017-0283-5
856	4 8	u https://doi.org/10.1186/s12984-017-0283-5
856	4 8	u https://research.chalmers.se/publication/250892

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Av författaren/redakt...: Nilsson, Niclas; Håkansson, Bo, 1 ...; Ortiz Catalan, M ...

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