# A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

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## Abstract

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## 1. Introduction

- signal-to-noise ratio
- noise signal

#### Noises in EMG Signals

#### Inherent Noise in Electronics Equipment

#### Ambient Noise

#### Motion Artifact

#### Inherent Instability of Signal

#### Electrocardiographic (ECG) Artifacts

#### Cross Talk

## 2. Motivation

#### 2.1. Isotonic

#### 2.1.1. Concentric Contractions

#### 2.1.2. Eccentric Contractions

- Absolute tensions achieved are very high relative to the muscle’s maximum tetanic tension generating capacity.
- Absolute tension is relatively independent of lengthening velocity.

#### 2.2. Isometric

## 3. Research Methodology

## 4. Automated EMG Analysis

#### 4.1. EMG Signal Preprocessing

#### 4.2. EMG Feature Extraction and Selection

- Feature projection
- Feature selection

#### 4.3. Probability Density Function

#### 4.4. EMG Classification

#### 4.5. EMG Evaluation Metrics

## 5. EMG Dataset

#### 5.1. Placement of Electrodes

#### 5.2. Muscle Conditions

## 6. Discussion

## 7. Future Trends

- Most studies do not critically highlight the pre-processing stage. To remove the artifacts in EMG signals, the cutoff frequency should be in the range between 20 Hz to 500 Hz and a Butterworth filter commonly applied. Meanwhile, the segmentation techniques used should depend upon its applications. However, the comparison performance between adjacent and overlapped windowing technique has not yet been identified.
- Feature extraction is the most difficult part in motion pattern recognition. In the literature, they compared the performance of TD, FD and TFD features of EMG signals during isometric contractions. The features of muscle contractions under isotonic has yet to be explored.
- Sample data of EMG signals has always been fitted with PDF under physical situations which correspond to non-fatiguing conditions, also called as isometric contractions. However, the PDF of isotonic contractions for EMG signals has never been investigated.
- To achieve better classification accuracy, significant features extracted is the main contribution. A classifier can be chosen depending on the number of features. Different classifiers result in different percentage error.
- Pattern recognition of EMG signals for upper limbs has been widely investigated compared to lower limbs especially in isometric contraction. The proper isotonic contractions behind the generation of the EMG signals is still unknown.

## 8. Conclusions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Block diagram in a control system using a gait pattern generator and EMG signals adopted from [1].

**Figure 7.**General block diagram for FL systems [35].

Features | Abbreviation | References |
---|---|---|

Integrated EMG | IEMG | [28] |

Mean Absolute Value | MAV | [7,28,42,47,50] |

Modified mean absolute value 1 | MAV1 | [28,51] |

Modified mean absolute value 2 | MAV2 | [28,51] |

Root Mean Square | RMS | [7,13,28,47] |

Variance | VAR | [28,47] |

Waveform length | WL | [28,42,50] |

Zero crossing | ZC | [28,42,50] |

Slope sign change | SSC | [28,42,47] |

Willison amplitude or Wilson amplitude | WAMP | [28,47] |

Kurtosis | KURT | [31] |

Skewness | SKEW | [52] |

Moving Approximate Entropy | moving ApEn | [35] |

Fuzzy approximate entropy | fApEn | [48] |

Simple square integral | SSI | [28] |

v-Order | V | [28,50] |

Log detector | LOG | [28] |

Average amplitude change | AAC | [28] |

Difference absolute standard deviation value | DASDV | [28] |

Mean absolute value slope | MAVSLP | [28] |

Multiple hamming windows | MHW | [28] |

Multiple trapezoidal windows | MTW | [28] |

Histogram of EMG | HIST | [50] |

Auto-regressive coefficients | AR | [50] |

Cepstral coefficients | [28] | |

Standard deviation | SD | [7,42,47] |

Cepstral coefficients | CC | [28] |

Sample entropy | SampEn | [53] |

Integral absolute value | IAV | [50] |

Variance | VAR | [50] |

Maximum amplitude | MAX | [7] |

Features | Abbreviation | References |
---|---|---|

Mean frequency | MNF | [28,41] |

Median frequency | MDF | [28,41] |

Mean power frequency | MNP | [51] |

Peak frequency | PKF | [28] |

Total power | TTP | [28] |

Frequency ratio | FR | [28] |

Power spectrum ratio | PSR | [28] |

The power spectrum deformation | Ω | [41] |

Variance of central frequency | VCF | [28] |

Signal-to-motion artifact ratio | SMR | [41] |

Signal-to-noise ratio | SNR | [41] |

Spectral moment | SM | [28] |

Energy | EN | [42] |

Wavelet decomposition | WDC | [42] |

Wavelet decomposition difference | WDCDIF | [42] |

Modified mean frequency | MMNF | [56] |

Modified median frequencies | MMDF | [56] |

Short Time Fourier transform | STFT | [57] |

Features | Abbreviation | References |
---|---|---|

Discrete Wavelet Transform | DWT | [44] |

Continous Wavelet Transform | CWT | [9] |

Empirical Mode Decomposition | EMD | [9] |

Wavelet Packet Transform | WPT | [44] |

**Table 4.**The mean of the MAE features of each theoretical PDF for each percentile load level condition.

Maximum Voluntary Contraction | |||||
---|---|---|---|---|---|

20% | 40% | 60% | 80% | 100% | |

Normal | 0.0036 | 0.0025 | 0.0024 | 0.0022 | 0.0028 |

Laplace | 0.0081 | 0.0075 | 0.0076 | 0.0077 | 0.0071 |

Cauchy | 0.0129 | 0.0123 | 0.0123 | 0.0124 | 0.0122 |

Logistic | 0.0027 | 0.0012 | 0.0009 | 0.0011 | 0.0015 |

Authors | |
---|---|

Gaussian | [43,76,77,78,85] |

Cauchy | [65] |

Laplace | [76,77] |

Logistic | [65] |

GEV | [81] |

**Table 6.**The classification accuracy based on training functions [98].

Training | Stop | Regression | Time | Classification Rate | Hidden | |||
---|---|---|---|---|---|---|---|---|

Function | Epochs | Elapsed | Training | Validation | Test | Overall | Neurons | |

Levenberg marquardt | 15 | 0.8597 | 1.047 | 88.6 | 83.3 | 90 | 88 | 10 |

18 | 0.87251 | 0.921 | 94.3 | 66.7 | 80 | 88 | ||

16 | 0.87401 | 0.8721 | 88.7 | 90.3 | 90.3 | 89.2 | ||

Average | 0.86874 | 0.947 | 90.533 | 80.1 | 86.767 | 88.4 | ||

33 | 0.85706 | 2.797 | 91.4 | 70 | 83.3 | 87 | 20 | |

14 | 0.85508 | 1.218 | 90 | 80 | 86.7 | 88 | ||

12 | 0.84772 | 1.094 | 92.9 | 76.7 | 83.3 | 89 | ||

Average | 0.853287 | 1.703 | 91.433 | 75.567 | 84.433 | 88 | ||

16 | 0.86112 | 2.36 | 92.1 | 80 | 76.7 | 88 | 30 | |

11 | 0.85018 | 1.703 | 91.4 | 90 | 73.3 | 88.5 | ||

14 | 0.85192 | 2.125 | 89.3 | 76.7 | 83.3 | 86.5 | ||

Average | 0.854107 | 2.0627 | 90.933 | 82.233 | 77.767 | 87.667 | ||

Scaled Conjugate Gradient | 37 | 0.7819 | 0.703 | 80.7 | 83.3 | 83.3 | 82.43 | 10 |

27 | 0.7632 | 0.685 | 78.2 | 86 | 74.5 | 79.57 | ||

32 | 0.7904 | 0.823 | 82.4 | 71.9 | 79.4 | 77.9 | ||

Average | 0.77917 | 0.737 | 80.433 | 80.4 | 79.067 | 79.9 | ||

31 | 0.802 | 0.797 | 78.6 | 90 | 82.7 | 83.77 | 20 | |

35 | 0.8153 | 1.252 | 79 | 87.3 | 78.1 | 81.47 | ||

34 | 0.79842 | 1.063 | 84.3 | 76.7 | 80 | 80.33 | ||

Average | 0.80524 | 1.037 | 80.633 | 84.667 | 80.267 | 81.86 | ||

34 | 0.80767 | 2.457 | 83.6 | 83.3 | 86.7 | 84.53 | 30 | |

28 | 0.79215 | 1.073 | 81.2 | 72.1 | 69.5 | 74.27 | ||

31 | 0.82531 | 1.352 | 86.6 | 76.5 | 78.8 | 80.63 | ||

Average | 0.80837 | 1.627 | 80.433 | 80.4 | 79.067 | 79.9 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Nazmi, N.; Abdul Rahman, M.A.; Yamamoto, S.-I.; Ahmad, S.A.; Zamzuri, H.; Mazlan, S.A. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. *Sensors* **2016**, *16*, 1304.
https://doi.org/10.3390/s16081304

**AMA Style**

Nazmi N, Abdul Rahman MA, Yamamoto S-I, Ahmad SA, Zamzuri H, Mazlan SA. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. *Sensors*. 2016; 16(8):1304.
https://doi.org/10.3390/s16081304

**Chicago/Turabian Style**

Nazmi, Nurhazimah, Mohd Azizi Abdul Rahman, Shin-Ichiroh Yamamoto, Siti Anom Ahmad, Hairi Zamzuri, and Saiful Amri Mazlan. 2016. "A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions" *Sensors* 16, no. 8: 1304.
https://doi.org/10.3390/s16081304