# Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

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## Abstract

**:**

## 1. Introduction

- The acquisition of one-channel sEMG was carried out with a sampling rate of 1 kHz. The acquired signals were processed using 13-time domain and 3-frequency domain features that made them easier to process.
- All 16-features were reduced using PCA into two and three-dimensional space before processing to the classifier. The results from PCA could clearly show the separated pattern in 3-dimensional space for each subject.
- The proposed method was able to differentiate nine-classes of hand gestures with higher accuracy, especially for each subject classification.

## 2. Related Work

## 3. Methods

#### 3.1. Feature Extraction

- MAV1 (modified mean absolute value type 1): MAV1 is an extension of the MAV feature [21,22]:$$\begin{array}{l}\mathrm{MAV}1=\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{w}_{i}\left|{x}_{i}\right|}\\ {w}_{i}=\{\begin{array}{ll}1,\hfill & \mathrm{if}0.25N\le i\le 0.75N\hfill \\ 0.5,\hfill & \mathrm{otherwise}\hfill \end{array}\end{array}$$
- MAV2 (modified mean absolute value type 2): MAV2 is an expansion of the MAV1 feature, which was used in [21,22]:$$\begin{array}{l}\mathrm{MAV}2=\frac{1}{N}{\displaystyle \sum _{i=1}^{N}{w}_{i}\left|{x}_{i}\right|}\\ {w}_{i}=\{\begin{array}{ll}1,\hfill & \mathrm{if}0.25N\le i\le 0.75N\hfill \\ 4i/N,\hfill & \mathrm{else}\mathrm{if}i0.25N\hfill \\ 4(i-N)/N,\hfill & \mathrm{otherwise}\hfill \end{array}\end{array}$$
- SSI (simple square integral) or integral square is a feature that calculates the energy of the EMG signal [23]:$$\mathrm{SSI}={\displaystyle \sum _{i=1}^{N}{x}_{i}^{2}}$$
- DASDV (difference absolute standard deviation value): DASDV is a standard deviation value of the wavelength [26]:$$\mathrm{DASDV}=\sqrt{\frac{1}{N-1}{\displaystyle \sum _{i=1}^{N-1}{\left({x}_{i+1}+{x}_{i}\right)}^{2}}}$$
_{1}, x_{2}, … x_{N}and N refers to the number of samples of each digitized sEMG signal.

- Hjorth 1 (Activity): Hjorth 1 measures the surface of power spectrum in the frequency domain [27].$$\mathrm{H}1=\mathrm{var}(x)$$
- Hjorth 2 (Mobility): Hjorth 2 calculates the mean frequency, or the standard deviation of the power spectrum [27]:$$\mathrm{H}2=\sqrt{\frac{\mathrm{var}\left(x\frac{dx}{dt}\right)}{\mathrm{var}(x)}}$$
- Hjorth 3 (Complexity): Hjorth 3 measures the change in frequency by comparing the signal’s similarity to a pure sine wave [26]:$$\mathrm{H}3=\frac{mobility\left(x\frac{dx}{dt}\right)}{mobility(x)}$$

#### 3.2. Feature Reduction: Principal Component Analysis (PCA)

**x**

_{t}(t = 1, …, l and Σ

**x**

_{t}= 0), each of which is of m dimension

**x**

_{t}= [x

_{t}(1), x

_{t}(2), …, x

_{t}(m)]

^{T}, ordinarily m < l, ${s}_{t}$ linearly transforms each vector ${x}_{t}$ as in Equation (14):

**U**is the m × m orthogonal matrix whose ith column,

**u**

_{i}is the eigenvector of the sample covariance matrix

**C**. The

**C**matrix can be calculated using Equation (15):

_{i}is one of the eigenvalues of

**C**. The components of ${s}_{t}$ are then calculated as the orthogonal transformations of ${x}_{t}$ based on the estimated ${u}_{i}$

#### 3.3. Feature Classification: Artificial Neural Network (ANN)

_{1}is the real output in classification; (2) y

_{2}is the output from ANN classification; and (3) m is the total number of samples in classification. The ANN structure in this study had three inputs to classify nine classes. Details of the inputs and the classes are presented in Figure 4.

#### 3.4. Experimental Setup

## 4. Results and Discussion

#### 4.1. Principle Component Analysis (PCA)

#### 4.2. Artificial Neural Network (ANN)

#### 4.3. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Neuron activation function: (

**a**) tansig activation function; (

**b**) softmax activation function.

**Figure 5.**Subject in the EMG experiment and the EMG sensor location on the flexor digitorum superficialis.

**Figure 6.**Principle component analysis (PCA) results. (

**a**) Subject 1; (

**b**) Subject 2; (

**c**) Subject 3; (

**d**) Subject 4; (

**e**) Subject 5.

Selected Literature | No. of Finger Movements | Feature Extraction | Classification Algorithm | EMG Channel/Fs | Accuracy of Classification |
---|---|---|---|---|---|

Ariyanto et al. (2015) [5] | 5 (thumb, little, ring, middle, index) | 16 (IEMG, SSI, VAR, RMS, WL, MAV, MAV1, MAV2 DASDV, AR, Hjorth1, Hjorth2, Hjorth3) | ANN | 1 channel/4 kHz | 96.7% |

Xing et al. (2014) [6] | 7 (unknown finger movement) | Wavelet package transform (WPT) WPT | SVM KNN | 4 channel/1024 Hz | 98.39% 97.5% |

Riillo et al. (2014) [7] | 5 (rest, fist, pinch, spread, pointing) | RMS-WA Willison Amplitudo (WA) M-RMS-(WA) | ANN ANN | 6 channel/1 kHz | 88% 89% |

Phinyomark et al. (2012) [8] | 6 (hand open, hand close, wrist extension, wrist flex, forearm pronation, forearm supination) | IEMG, MAV, MAV1, MAV2, SSI, VAR, Absolute Temporal Moment 3 (TM3), TM4, TM5, RMS, v-order (V), Log detector (LOG), WL, Average amplitude change (AAC), DASDV, Zero crossing (ZC), Amplitude of the first burst (AFB), Myopulse percentage rate (MYOP), Willison amplitude (WAMP), Slope sign change (SSC), Mean absolute value slope (MAVSLP), Multiple hamming windows (MHW), Multiple trapezoidal windows (MTW), Histogram (HIST), AR, Cepstral coefficients (CC), Total power (TTP), Spectral moment 1 (SM1), Spectral moment 2 (SM2), Spectral moment 3 (SM3), Mean frequency (MNF), Median frequency (MDF), Peak frequency (PKF), Mean power (MNP), Frequency ratio (FR), Power spectrum ratio (PSR), Variance of central frequency (VCF) | SVM KNN | 2 channel/1 kHz | 98.39% 97.5% |

Kushaba et al. (2012) [9] | 10 (thumb, index, middle, ring, little, thumb-index, thumb little, thumb-ring, thumb-middle, hand close) | Slope sign change (SSC), ZC, WL, AR, Hjorth parameters, Amady and Horwat, Sample Skewness (SS), AR | LIBSVM KNN | 2 channel/4 kHz | Approx. 92% Approx. 91% |

Balbinot et al. (2013) [10] | 7 (hand contraction, forearm rotation, hand abduction, hand adduction, wrist extension, wrist flexion, forearm flexion,) | RMS | Neuro-Fuzzy | 8 channel/1 kHz | 86% |

Mane et al. (2015) [11] | 3 (open hand, close hand, wrist extensor) | WPT | ANN | 1 channel/1 kHz | 93.25% |

Lu et al. (2015) [12] | 10 (open mobile phone, screw open bottle, take a coin and move to the palm, screw to open a big bottle using all five finger, roll a small cylinder, pick up a scissor and cut paper, pencil flips, remove the pencil from back of front for writing, pick up a pencil and simply rotate to write, pick up a pencil and complexly rotate to write. | AR, the autoregressive moving average (ARMA), integrated moving average (ARIMA), Wavelet, RMS, WAMP, motor unit action potential (MUAP) | Expectation Maximation (EM) | 16 channel/NA | 95% |

Coelho et al. (2014) [13] | 6 (unknown finger postures) | Fractal dimension | NA | 8 channel/3 kHz | NA |

Shin et al. (2014) [14] | 6 (hand close, hand open, forearm pronation, forearm supination, wrist flexion, wrist extension, rest state) | TD, MAV, WL, ZC, SSC, AR4, RMS, AR6, AR4, AR6 | SVM | 2 channel/1 kHz | NA |

Wu et al. (2018) [15] | 5 (bend wrist down, bend wrist up, bend wrist down while in shake hand, bend wrist up while in shake hand, fist) | short time energy (EK), T (activity duration), EK, TH, T (activity duration), IEMG, MAV, VAR, SD, E, MAX, SSC, SK, KU | KNN SVM | 1 channel/2 kHz | 75.8% (KNN) 79.8% (SVM) |

Advantages | Disadvantages |
---|---|

Because it utilizes only one-channel of the sEMG sensor, the acquisition can be reduced in terms of cost and complexity. | The placement of the sEMG sensor pad needs to be attached carefully and correctly. |

The algorithm for feature computation is easy to compute and has a faster processing speed because most of the features use time domain features. | It needs sixteen features before they can be reduced into two or three features using PCA dimension reduction. |

The acquisition uses a sampling rate of 1000 Hz for discriminating nine hand gestures. | The wireless data acquisition needs Bluetooth 3.0 technology and above. |

Subject | Sex | Age (years) | Height (cm) | Weight (kg) | Hand |
---|---|---|---|---|---|

Subject 1 | Female | 30 | 164 | 60 | Right |

Subject 2 | Female | 21 | 165 | 61 | Right |

Subject 3 | Male | 22 | 169 | 70 | Right |

Subject 4 | Male | 21 | 168 | 69 | Left |

Subject 5 | Male | 35 | 168 | 70 | Right |

Finger Movement | Image | Example of EMG Signal |
---|---|---|

1. Tripod | ||

2. Power | ||

3. Active index | ||

4. Precision closed | ||

5. Precision open | ||

6. Finger point | ||

7. Mouse | ||

8. Hand open | ||

9. Hand closed |

Utilized PCs | ANN Accuracy Results (%) | |||
---|---|---|---|---|

Training | Validation | Testing | Overall | |

All PC (PC1–PC3) | 92.4 | 70.6 | 76.9 | 86.7 |

Selected PCs | 75.2 | 50 | 58.8 | 68.9 |

Selected PCs | Subject | ANN Accuracy Results (%) | |||
---|---|---|---|---|---|

Training | Validation | Testing | Overall | ||

All PCs (PC1–PC3) | 1 | 64.5 | 42.9 | 57.1 | 60 |

All PCs (PC1–PC3) | 2 | 38.7 | 57.1 | 14.3 | 37.8 |

All PCs (PC1–PC3) | 3 | 96.8 | 85.7 | 71.4 | 91.1 |

All PCs (PC1–PC3) | 4 | 90.3 | 57.1 | 57.1 | 80 |

All PCs (PC1–PC3) | 5 | 100 | 100 | 85.7 | 97.8 |

PC2–PC3 | 1 | 96.8 | 100 | 85.7 | 95.6 |

PC2–PC3 | 2 | 100 | 57.1 | 71.4 | 88.9 |

PC2–PC3 | 3 | 93.5 | 85.7 | 57.1 | 86.7 |

PC2–PC3 | 4 | 100 | 85.7 | 71.4 | 93.3 |

PC2–PC3 | 5 | 100 | 100 | 100 | 100 |

Finger Movement | Features Extraction | Classification Algorithm | EMG Channel/Fs | Accuracy of Classification |
---|---|---|---|---|

9 (tripod, power, active index, precision closed, precision open, finger point, mouse, hand open, hand closed) | 16 (IEMG, MAV, MAV1, MAV2, SSI, VAR, RMS, WL, DASDV, AR, Hjorth1, Hjorth2, Hjorth3) | ANN | 1 channel/1 kHz | 86.7% for all subject average 92.9% using selected PC for an individual subject |

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**MDPI and ACS Style**

Arozi, M.; Caesarendra, W.; Ariyanto, M.; Munadi, M.; Setiawan, J.D.; Glowacz, A.
Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements. *Symmetry* **2020**, *12*, 541.
https://doi.org/10.3390/sym12040541

**AMA Style**

Arozi M, Caesarendra W, Ariyanto M, Munadi M, Setiawan JD, Glowacz A.
Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements. *Symmetry*. 2020; 12(4):541.
https://doi.org/10.3390/sym12040541

**Chicago/Turabian Style**

Arozi, Moh, Wahyu Caesarendra, Mochammad Ariyanto, M. Munadi, Joga D. Setiawan, and Adam Glowacz.
2020. "Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements" *Symmetry* 12, no. 4: 541.
https://doi.org/10.3390/sym12040541