Fluctuating EMG Signals: Investigating Long-term Effects of Pattern Matching Algorithms

Abstract

In this paper, we investigate the behavior of state-of-the-art pattern matching algorithms when applied to electromyographic data recorded during 21 days. To this end, we compare the five classification techniques k-nearest-neighbor, linear discriminant analysis, decision trees, artificial neural networks and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize ten different hand movements. The major result of our investigation is that the classification accuracy of initially trained pattern matching algorithms might degrade on subsequent data indicating variations in the electromyographic signals over time.

Addresses

Faculty of Electrical Engineering, Computer Science and Mathematics, University of Paderborn, Germany. paul.kaufmann@upb.de

Autoren

Paul Kaufmann
Kevin Englehart
Marco Platzner

DOI

10.1109/iembs.2010.5627288

eISSN

2694-0604

Externe Identifier

PubMed Identifier: 21096692

Open access

false

ISSN

2375-7477

Zeitschrift

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Schlüsselwörter

Humans
Electromyography
Algorithms
Pattern Recognition, Automated

Sprache

eng

Medium

Print

Paginierung

6357 - 6360

Datum der Veröffentlichung

2010

Status

Published

Datum der Datenerfassung

2010

Titel

Fluctuating emg signals: investigating long-term effects of pattern matching algorithms.

Sub types

Research Support, Non-U.S. Gov't
Journal Article

Ausgabe der Zeitschrift

2010

Datenquelle: Europe PubMed Central

Abstract

In this paper, we investigate the behavior of state-of-the-art pattern matching algorithms when applied to electromyographic data recorded during 21 days. To this end, we compare the five classification techniques k-nearest-neighbor, linear discriminant analysis, decision trees, artificial neural networks and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize ten different hand movements. The major result of our investigation is that the classification accuracy of initially trained pattern matching algorithms might degrade on subsequent data indicating variations in the electromyographic signals over time.

Autoren

Paul Kaufmann
Kevin Englehart
Marco Platzner

Autoren-URL

https://www.ncbi.nlm.nih.gov/pubmed/21096692

DOI

10.1109/IEMBS.2010.5627288

ISSN

2375-7477

Zeitschrift

Annu Int Conf IEEE Eng Med Biol Soc

Schlüsselwörter

Algorithms
Electromyography
Humans
Pattern Recognition, Automated

Sprache

eng

Country

United States

Paginierung

6357 - 6360

Datum der Veröffentlichung

2010

Status

Published

Datum, an dem der Datensatz öffentlich gemacht wurde

2011

Titel

Fluctuating emg signals: investigating long-term effects of pattern matching algorithms.

Sub types

Journal Article
Research Support, Non-U.S. Gov't

Ausgabe der Zeitschrift

2010

Datenquelle: PubMed

Abstract

In this paper, we investigate the behavior of state-of-the-art pattern matching algorithms when applied to electromyographic data recorded during 21 days. To this end, we compare the five classification techniques k-nearest-neighbor, linear discriminant analysis, decision trees, artificial neural networks and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize ten different hand movements. The major result of our investigation is that the classification accuracy of initially trained pattern matching algorithms might degrade on subsequent data indicating variations in the electromyographic signals over time.

Autoren

Paul Kaufmann
Kevin Englehart
Marco Platzner

Zeitschrift

32nd Intl. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC)

Notes

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Paginierung

6357 - 6360

Datum der Veröffentlichung

2010

Herausgeber

IEEE

Datum der Datenerfassung

2020

Titel

Fluctuating EMG Signals: Investigating Long-term Effects of Pattern Matching Algorithms

Sub types

inproceedings

Datenquelle: Manual

Fluctuating EMG Signals: Investigating Long-term Effects of Pattern Matching Algorithms

Werkzeuge