Classification complexity in myoelectric pattern recognition

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Nilsson, NiclasChalmers tekniska högskola,Chalmers University of Technology (författare)

Classification complexity in myoelectric pattern recognition

Artikel/kapitelEngelska2017

Förlag, utgivningsår, omfång ...

2017-07-10
Springer Science and Business Media LLC,2017
electronicrdacarrier

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LIBRIS-ID:oai:research.chalmers.se:c6be84e0-7b9f-4675-a16c-248cfd67d069
https://doi.org/10.1186/s12984-017-0283-5DOI
https://research.chalmers.se/publication/250892URI

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Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:art swepub-publicationtype
Ämneskategori:ref swepub-contenttype

Anmärkningar

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.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Medicinteknik hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering hsv//eng
Prosthesis control
Electromyography
Myoelectric pattern recognition
Classification complexity

Biuppslag (personer, institutioner, konferenser, titlar ...)

Håkansson, Bo,1953Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)boh (författare)
Ortiz Catalan, Max Jair,1982Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)maxo (författare)
Chalmers tekniska högskola (creator_code:org_t)

Sammanhörande titlar

Ingår i:Journal of NeuroEngineering and Rehabilitation: Springer Science and Business Media LLC14:1, s. Article no. 68 -1743-0003

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Journal of NeuroEngineering and Rehabilitation (Sök värdpublikationen i LIBRIS)

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

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Medicinteknik

Artiklar i publikationen: Journal of Neuro ...

Av lärosätet: Chalmers tekniska högskola

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