EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi

Cagri Cerci; Hakan Temeltas

doi:10.1109/SIU.2018.8404207

EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi

Translated title of the contribution: Feature extraction of EMG signals, classification with ANN and kNN algorithms

Cagri Cerci, Hakan Temeltas

Department of Control and Automation Engineering

Gedik University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

This study aims to classify the electrical signals generated by the movement of muscles. The data set contains eight different motion signals from four different individuals. Classified motions are thumb, index, middle, ring, thumb-index, thumb-middle, thumb-ring and hand close. Firstly, the signal is divided into small parts by windowing process, and the feature vectors are created by applying various feature extraction methods to these small signals. Then, the feature vectors are classified by k-nearest neighbors and artificial neural networks algorithm. At the end of these processes, the classification accuracy was 89% for kNN at 150 ms and 93% for ANN. The advantage of the kNN Algorithm is that the processing time is shorter than ANN.

Translated title of the contribution	Feature extraction of EMG signals, classification with ANN and kNN algorithms
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404207
Publication status	Published - 5 Jul 2018
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

Name	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/SIU.2018.8404207

Cite this

Cerci, C., & Temeltas, H. (2018). EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404207

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title = "EMG Sinyallerinin {\"O}zniteliklerinin {\c C}ikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi",

abstract = "This study aims to classify the electrical signals generated by the movement of muscles. The data set contains eight different motion signals from four different individuals. Classified motions are thumb, index, middle, ring, thumb-index, thumb-middle, thumb-ring and hand close. Firstly, the signal is divided into small parts by windowing process, and the feature vectors are created by applying various feature extraction methods to these small signals. Then, the feature vectors are classified by k-nearest neighbors and artificial neural networks algorithm. At the end of these processes, the classification accuracy was 89% for kNN at 150 ms and 93% for ANN. The advantage of the kNN Algorithm is that the processing time is shorter than ANN.",

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author = "Cagri Cerci and Hakan Temeltas",

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Cerci, C & Temeltas, H 2018, EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404207

EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi. / Cerci, Cagri; Temeltas, Hakan.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi

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AU - Temeltas, Hakan

PY - 2018/7/5

Y1 - 2018/7/5

N2 - This study aims to classify the electrical signals generated by the movement of muscles. The data set contains eight different motion signals from four different individuals. Classified motions are thumb, index, middle, ring, thumb-index, thumb-middle, thumb-ring and hand close. Firstly, the signal is divided into small parts by windowing process, and the feature vectors are created by applying various feature extraction methods to these small signals. Then, the feature vectors are classified by k-nearest neighbors and artificial neural networks algorithm. At the end of these processes, the classification accuracy was 89% for kNN at 150 ms and 93% for ANN. The advantage of the kNN Algorithm is that the processing time is shorter than ANN.

AB - This study aims to classify the electrical signals generated by the movement of muscles. The data set contains eight different motion signals from four different individuals. Classified motions are thumb, index, middle, ring, thumb-index, thumb-middle, thumb-ring and hand close. Firstly, the signal is divided into small parts by windowing process, and the feature vectors are created by applying various feature extraction methods to these small signals. Then, the feature vectors are classified by k-nearest neighbors and artificial neural networks algorithm. At the end of these processes, the classification accuracy was 89% for kNN at 150 ms and 93% for ANN. The advantage of the kNN Algorithm is that the processing time is shorter than ANN.

KW - Artificial Neural Networks

KW - Classification

KW - Feature Extraction

KW - K-nearest neighbors

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M3 - Konferans katkısı

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Cerci C, Temeltas H. EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4. (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). doi: 10.1109/SIU.2018.8404207

EMG Sinyallerinin Özniteliklerinin Çikarilmasi, YSA ve kNN Algoritmalariyla Siniflandirilmasi

Abstract

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