A review of classification techniques of EMG signals during isotonic and isometric contractions

Nurhazimah Nazmi, Mohd Azizi Abdul Rahman, Shin Ichiroh Yamamoto, Siti Anom Ahmad, Hair Zamzuri, Saiful Amri Mazlan

Research output: Research - peer-reviewReview article

  • 7 Citations

Abstract

In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

LanguageEnglish
Article number1304
JournalSensors
Volume16
Issue number8
DOIs
StatePublished - 2016 Aug 17

Fingerprint

electromyography
contraction
Electromyography
classifying
pattern recognition
Patient rehabilitation
Feature extraction
signal analysis
preprocessing
research and development
probability density functions
therapy
education
engineering
methodology
Physical therapy
Signal analysis
Probability density function
Processing

Keywords

  • Classifications
  • EMG signals
  • Feature extractions
  • Isometric contractions
  • Isotonic contractions
  • Probability density functions

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Electrical and Electronic Engineering

Cite this

Nazmi, N., Rahman, M. A. A., Yamamoto, S. I., Ahmad, S. A., Zamzuri, H., & Mazlan, S. A. (2016). A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors, 16(8), [1304]. DOI: 10.3390/s16081304

A review of classification techniques of EMG signals during isotonic and isometric contractions. / Nazmi, Nurhazimah; Rahman, Mohd Azizi Abdul; Yamamoto, Shin Ichiroh; Ahmad, Siti Anom; Zamzuri, Hair; Mazlan, Saiful Amri.

In: Sensors, Vol. 16, No. 8, 1304, 17.08.2016.

Research output: Research - peer-reviewReview article

Nazmi, N, Rahman, MAA, Yamamoto, SI, Ahmad, SA, Zamzuri, H & Mazlan, SA 2016, 'A review of classification techniques of EMG signals during isotonic and isometric contractions' Sensors, vol 16, no. 8, 1304. DOI: 10.3390/s16081304
Nazmi N, Rahman MAA, Yamamoto SI, Ahmad SA, Zamzuri H, Mazlan SA. A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors. 2016 Aug 17;16(8). 1304. Available from, DOI: 10.3390/s16081304
Nazmi, Nurhazimah ; Rahman, Mohd Azizi Abdul ; Yamamoto, Shin Ichiroh ; Ahmad, Siti Anom ; Zamzuri, Hair ; Mazlan, Saiful Amri. / A review of classification techniques of EMG signals during isotonic and isometric contractions. In: Sensors. 2016 ; Vol. 16, No. 8.
@article{7ac75e261f28455d99019def4664e4c7,
title = "A review of classification techniques of EMG signals during isotonic and isometric contractions",
abstract = "In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.",
keywords = "Classifications, EMG signals, Feature extractions, Isometric contractions, Isotonic contractions, Probability density functions",
author = "Nurhazimah Nazmi and Rahman, {Mohd Azizi Abdul} and Yamamoto, {Shin Ichiroh} and Ahmad, {Siti Anom} and Hair Zamzuri and Mazlan, {Saiful Amri}",
year = "2016",
month = "8",
doi = "10.3390/s16081304",
volume = "16",
journal = "Sensors",
issn = "1424-3210",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "8",

}

TY - JOUR

T1 - A review of classification techniques of EMG signals during isotonic and isometric contractions

AU - Nazmi,Nurhazimah

AU - Rahman,Mohd Azizi Abdul

AU - Yamamoto,Shin Ichiroh

AU - Ahmad,Siti Anom

AU - Zamzuri,Hair

AU - Mazlan,Saiful Amri

PY - 2016/8/17

Y1 - 2016/8/17

N2 - In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

AB - In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

KW - Classifications

KW - EMG signals

KW - Feature extractions

KW - Isometric contractions

KW - Isotonic contractions

KW - Probability density functions

UR - http://www.scopus.com/inward/record.url?scp=84983487440&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84983487440&partnerID=8YFLogxK

U2 - 10.3390/s16081304

DO - 10.3390/s16081304

M3 - Review article

VL - 16

JO - Sensors

T2 - Sensors

JF - Sensors

SN - 1424-3210

IS - 8

M1 - 1304

ER -