# Expert System for Real Time Arm Movement Recognition Based on Surface EMG Signal

Shripad Bhatlawande, Swati Shilaskar, Mahesh Kinge, Anurag Mahajan

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Shripad Bhatlawande, Swati Shilaskar, Mahesh Kinge, Anurag Mahajan. Expert System for Real Time Arm Movement Recognition Based on Surface EMG Signal. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.11-24, 10.1007/978-3-031-38296-3_2. hal-05466042

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# Expert System for Real Time Arm Movement Recognition Based on Surface EMG Signal

Shripad Bhatlawande, Swati Shilaskar, Mahesh Kinge(✉), and Anurag Mahajan

Department of E and TC Engineering, VIT, Pune 411037, India
{shripad.bhatlawande,swati.shilaskar,mahesh.kinge19, anurag.mahajan19}@vit.edu

**Abstract.** This paper presents classification of Electromyography (EMG) signals to distinguish between upper-limb motions. The system can help in the movement of a prosthetic hand. Eight healthy subjects participated in the experiment. Total eight movements are performed on four distinct muscles to capture and analyze the signals. The movements performed are elbow extension, hand close, wrist extension, wrist flexion, hand open, ulnar deviation, elbow flexion and radial deviation. Sixteen statistical features are used to classify data. Sampling frequency used is 1024 Hz. To eliminate powerline frequency equal to 50 Hz, a bandstop filter is utilized. Bandpass filter is used with cut-off frequencies 50 Hz and 160 Hz. An array of 6 classifiers is used such as Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT) and Random Forest (RF). A voting classifier (VC) is used to explore consensus-based classification and detection. Metrics used are F1 score, accuracy, precision and recall. Average accuracy achieved is 70.36%, average precision is 73.36%, average recall is 70.36% and average F1 score achieved is 69.56%.

**Keywords:** Electromyography · Hand Extension · Hand Flexion · Ulnar Deviation · Radial Deviation · Expert System for amputee

## 1 Introduction

Amputation of the upper limb prevents [1] amputees from performing their arm functions during daily activities. Additionally, it lowers their quality of life. Electromyography (EMG) is a signal utilized for measuring muscle reaction or electrical impulses with nerve stimulation. Detection of irregularities in the neuromuscular system becomes possible due to this technique. The performance of the surface electromyography (sEMG)-interface [2] can be significantly hindered by fatty tissue and muscle cooperation in synthetic tests. Movements of the upper limb [3] in everyday activities are frequently employed for the control of robot arms. The approach used on sEMG signals should be able to detect these movements.
## 2 Literature Survey

A technique [4] was developed for identifying movements from sEMG data by combining digital signal processing, and discrete wavelet transform. This research employed sEMG signals to investigate the potential for monitoring motor rehabilitation progress. The electrodes were placed at flexor digitorum superficialis, triceps brachii (TB), biceps brachii (BB), extensor digitorum (ED) superficialis and around the radio-humeral joint. Deep learning (DL) approaches [5] were used to analyse EMG data. The analysis revealed the categorization of speech and emotion, hand gestures, and sleep stage.

A 50 Hz Notch filter and band pass filter [6] with a 10 Hz to 500 Hz cut-off frequency was used. Three time-domain (TD) features were proposed. Conventionally used TD features- slope sign changes (SSC), waveform length (WL), mean absolute value (MAV), zero crossings (ZC), auto-regressive coefficients (AR), Willison Amplitude (WAMP) and root mean square (RMS) were utilized. A set of characteristics [7] were proposed aimed at decreasing the force level changes having an impact on prosthesis controlled by amputees. Features used were the time-dependent power spectrum descriptors (TD-PSD), a combination of TD and autoregressive model parameters, wavelets, reduced spectral moments by Du, Vuskovic, and TD features. A deep learning-based approach [8] was proposed for estimating kinematic information from multi-channel EMG signals for myoelectric control. Hamming window was utilized for segmentation. The electrodes were placed at BB, TB, middle deltoid, posterior deltoid (PD) and anterior deltoid (AD. Recurrent neural networks (RNNs) performed better when compared to convolutional neural networks (CNN).

A myoelectric-based interface [9] that can recognize and categorize motions like shoulder and elbow was developed. The goal was to compare the performance of an extreme learning machine (ELM)-trained decoder for Features of EMG and synergy. A low pass filter with a 10 Hz cut-off frequency was used. Features extracted were MAV and variance (VAR). Electrodes were placed at BB, TB, AD, PD, pectoralis major (PM), infraspinatus and trapezius. An eight channel EMG system in [10] was used to record seven types of arm movements. A 4000 Hz sampling frequency band pass filter with a frequency range of 20 Hz to 500 Hz was employed. Electrodes were placed at extensor carpi ulnaris (ECU), extensor carpi radialis, longus extensor digitorum communis and flexor carpi radialis (FCR). The patients' sEMG signals [11] were segmented and categorized to help with sEMG signal categorization. In the experiment, ten subjects participated. The proposed deep backpropagation long short-term memory (BP – LSTM) for surface EMG signal classification was compared to a basic standard LSTM model. It was found [12] that the performance degradation of conventional electromyography-pattern recognition techniques of approximately 8.89% and 17.0% due to variations in muscle contraction force. Subject mobility could be reduced by using an optimal machine learning approach. The efficiency [13] of four alternative training data-collection protocols were categorized. Eight subjects participated in the experiment. Its accuracy based on several arm/wrist motions was shown. DNN-based systems [14] outperform classifiers such as RF, KNN and DT. sEMG signals collected from eleven subjects were utilized for the experiment. The two feature reduction techniques used were Principal component analysis (PCA) [15] and uncorrelated linear discriminant analysis. The results showed that the features for differentiating diverse upper-limb motions were willison amplitude
and waveform length. To boost the performance control of the upper-limb prosthesis [16], a classification algorithm incorporating sEMG and electroencephalography (EEG) data was developed. In the experiment, four subjects participated. The viability of combining sEMG and EEG signals to improve motion classification accuracy was demonstrated. ZC, SSC, MAV, and WL were the features extracted. Analysis [17] for motor intention prediction was provided in continuous limb motion based on EMG. It contained kinematic motion parameters, continuous motion estimation algorithms, and performance measures for system validation.

The classification accuracy [18] was investigated for eight primary upper limb phantom movements on the residual limb. High-pass and anti-aliasing low-pass filters were utilized to attain a passband between 10 Hz and 500 Hz. A butterworth filter of 4th order with 10 Hz cut-off frequency and 50 Hz notch filters were utilized. The classification accuracy [19] with Artificial Neural network (ANN) ranging from 60.9 to 93.0%. The addition of kinematic features increased accuracy by 4.8% on average. A 30–300 Hz bandpass filter (Butterworth filter) and a lowpass filter (Butterworth filter) of 4th order were used. Features such as MAV, WAMP, ZC, SSC, WL, RMS, MAVS, sample entropy, 4th order cepstral analysis (CC) and 4th auto-regressive model (AR) were extracted. For features such as ZC, SSC and WAMP, the threshold utilized was 1 µV. A blend of sEMG and near-infrared spectroscopy (NIRS) [20] was used for prosthetic manipulation. Data segmentation was performed with a window of 300 ms with an overlap of 200 ms. Extracted features were WL, SSC, MAV and ZC. Extensor carpi radialis longus, flexor carpi ulnaris, FCR and ED were the muscles at which the sensors were placed.

A wearable shirt was developed [21] that can be used to record EMG signals in the upper limbs. In the experiment, six subjects participated. The device can track EMG activity and give useful data in real-time to athletes, as well as their coaches, to help them improve their training procedures. An approach was developed [22] that allowed the identification of essential muscles contributing to motions. Selecting common muscles between healthy participants and patients is critical during the early phases of recovery. EMG data was acquired using 18 channels located at protanor teres, brachio radialis (BP), PM, triceps, PD, infra spinatus, biceps, elector spinae, and AD. The sampling frequency utilized was 1000 Hz. The signals were filtered between 0.2–0.3 Hz and were rectified using a bandpass filter between 0.1–32 Hz.

## 3 Methodology

An EMG machine of Clarity Ltd. And the software Octopus is utilized for acquiring EMG signals. There are 4 channels having 2 electrodes each which can be placed on muscles to acquire the signals. The surface electrodes are placed on FCR, ECU, BB, and TB for channels_1, 2, 3 and 4 respectively as depicted in Fig. 1.

In this experiment, 8 subjects participated. Movements performed include elbow_extension, hand_close, ulnar_deviation, wrist_flexion, elbow_flexion, hand_open, radial_deviation, and wrist extension (Fig. 2).

Sampling frequency of 1024 Hz is used. A bandstop filter (notch filter) is applied to eliminate 50 Hz frequencies (Fig. 4). Followed by it, a bandpass filter of 50 Hz and 160 Hz cutoff frequency is used to filter raw data (Fig. 5). Features like TD, TFD and FD, are
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Arrangement of electrodes</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Movements performed during the experiment</figcaption></figure>

extracted from the raw data (Table 1). Figure 3 depicts the 1D discrete wavelet transform of data for wrist extension. The first graph displays the original signal. Followed by it the other four graphs represent 4-dimensional coefficient arrays. The type of transform used is db2.

A windowing technique with a 500 samples window size and 100 step samples were utilized. Feature extraction techniques are used to attain TD, TFD and FD features.

The TD features (Eqs. 1 – 11) can be evaluated as follows:
<figure><img src="image_5.png" /><figcaption>Fig. 3. Discrete wavelet transform of wrist extension for FCR muscle.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Filtering the original EMG signal with notch filter</figcaption></figure>

VAR is a dispersion measure which means it gauges how widely apart a group of data are from their mean value.

$$ VAR = \frac{\sum_{z=1}^{Z} (y_m - \mu)^2}{z - 1} \qquad (1) $$

The square root of the sample points' mean square is known as RMS.

$$ RMS = \sqrt{\frac{1}{z} \sum_{m=1}^{z} y_m^2} \qquad (2) $$

IEMG is a mathematics integration of the EMG signal's absolute value.

$$ IEMG = \sum_{m=1}^{Z} |y_m| \qquad (3) $$

MAV refers for mean absolute deviation from the center. LD is exponential of MAV.

$$ MAV = \frac{1}{z} \sum_{m=1}^{z} |y_m| \qquad (4) $$
<table><caption>**Table 1.** Features extracted from raw data</caption><thead><tr><th>Features</th><th>DOMAIN</th></tr></thead><tbody><tr><td>Variance (VAR)</td><td rowspan="10">TIME-DOMAIN FEATURES</td></tr><tr><td>Root_mean_square (RMS)</td></tr><tr><td>Integrated_EMG (IEMG)</td></tr><tr><td>Mean_absentue_value (MAV)</td></tr><tr><td>Log Marginal (LD)</td></tr><tr><td>Waveform_length (WL)</td></tr><tr><td>Average_amplitude_change (AAC)</td></tr><tr><td>Difference_absentue_standard_deviation_value (DASDV)</td></tr><tr><td>Zero_c crossing (ZC)</td></tr><tr><td>Willison_amplitude (WAMP)</td></tr><tr><td>Myopulse_percentage_rate (MPR)</td></tr><tr><td>Frequency_ratio (FR)</td><td rowspan="6">FREQUENCY-DOMAIN FEATURES</td></tr><tr><td>Mean_power (MNP)</td></tr><tr><td>Total_power (TOT)</td></tr><tr><td>Mean_frequency (MNF)</td></tr><tr><td>Median_frequency (MDF)</td></tr><tr><td>Peak_frequency (PKF)</td></tr><tr><td>Discrete_wavelet_transform</td><td>TIME-FREQUENCY FEATURES</td></tr></tbody></table>

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Filtering the signal with bandpass filter</figcaption></figure>

$$LD = e^{\frac{\sum_{m=1}^{z} |y_m|}{z}} \quad (5)$$
AAC is the average of the absolute difference of the subsequent sample points, and WL is the absolute difference of the subsequent sample points.

$$WL = \sum_{m=1}^{Z} |y_m - y_{m-1}| \qquad (6)$$

$$AAC = \frac{\sum_{m=1}^{Z} |y_m - y_{m-1}|}{z} \qquad (7)$$

$$DASDV = \sqrt{\frac{1}{z-1} \sum_{m=1}^{Z} (y_m - y_{m-1})^2} \qquad (8)$$

WAMP is the resultant sample points' absolute difference with a certain condition as stated in the Eq. 9.

$$WAMP = \sum_{m=1}^{Z} |y_m - y_{m-1}| \qquad (9) \\ |y_m - y_{m-1}| = \{1, |y_m - y_{m-1}| \geq \text{threshold0, otherwise}\}$$

MPR is taking the average of the sample points with a condition as stated in the Eq. 10

$$MPR = \sum_{m=1}^{Z} y_m \qquad (10) \\ y_m = \{1, y_m \geq \text{threshold0, otherwise}\}$$

ZC is the measure of rate when the signal crosses zero when the signal changes from positive to negative or vice versa.

$$ZC = \sum_{m=2}^{Z} f((y_m - y_{m-1}) \times (y_m - y_{m-1})) \qquad (11)$$

where

z is the size of frame,

y are the sample points

The FD features (eqs. 12–17) can be evaluated as follows:

FR is used to distinguish signals of muscle compression and expansion

$$FR = \frac{\sum_{L}^{y} y_m}{\sum_{H}^{y} y_m} \qquad (12)$$

MNP and TOT are mean power and total power of an EMG signal respectively.

$$MNP = \frac{\sum_{m=1}^{Z} y_m}{z} \qquad (13)$$

$$TOT = \sum_{m=1}^{Z} y_m \qquad (14)$$

MNF, MDF and PKF are mean frequency, median frequency and peak frequency of an EMG signal respectively

$$MNF = \frac{\sum_{m=1}^{Z} (f_m * y_m)}{\sum_{m=1}^{Z} y_m} \qquad (15)$$
$$MDF = f_{\frac{z}{2}} \qquad (16)$$

$$PKF = f_{max(power)} \qquad (17)$$

where,

z = length of power signal

f = frequency

<figure><img src="image_8.png" /><figcaption>Fig. 6. Waveforms generated while performing upper limb movements</figcaption></figure>

Waveforms generated while performing different types of movements are depicted in Fig. 6. The amplitude vs time graphs are plotted where amplitude is in millivolts (mV) and the unit of time is 100 ms (ms). The overall flow of the system has been described in the methodology further (Fig. 7).

<figure><img src="image_9.png" /><figcaption>Fig. 7. Overall flow of the system for classification and detection of movements</figcaption></figure>

Dimensionality reduction has been used to reduce the number of features. This reduces the training time. The selection of features depends upon how much a particular feature influences the accuracy of a model. Standardization is used to scale the feature data. From each sample point, mean is subtracted, and variance is divided.
**Algorithm 1: Data filtering and feature extraction**

**Input:** Time-series data with indexing

**Output:** 184-dimensional feature vector

1. Sampling frequency of 1024Hz is used.

1. **for** filename in folder:

2. Bandstop filter with cut off frequencies of 49Hz and 51Hz for noise removal from power line.

3. Bandpass filter of 50Hz and 160Hz cut off frequencies used.

4. Apply windowing technique to generate features.

5. Calculate the TD, TFD and FD features.

6. Column-wise concatenate all the features extracted from data for every channel.

7. **end for**

8. Concatenate the feature data row-wise

9. Store the data

<figure><img src="image_10.png" /><figcaption>**Fig. 8.** a) Spectrogram of filtered EMG signal b) Spectrogram of filtered data with varying window sizes.</figcaption></figure>

Spectrograms have been plotted for visualizing the change in EMG signals frequency (Fig. 8). The frequency graphs obtained have been visualized with varying window sizes and overlapping segment size.

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** a) Boxplot of ZC feature b) Boxplot of WAMP feature</figcaption></figure>

Features such as ZC and WAMP features are able to distinguish between different movements as depicted in Fig. 9.
**Algorithm 2: Random Forest hyperparameter tuning**

**Input:** 184-dimensional feature vector

**Output:** Prediction of movement

1: Define the parameters max_depth = {2, 8, 16, 24} and n_estimators = {32, 64, 128, 256} that are to be used to tune the random forest model.

2: Define the classifier model and pass the parameters

3: Build a grid search

4: **for** i in max_depth:

5:     **for** j in n_estimators:

6:         Train the model with max_depth equal to i and n_estimators equal to j.

7:     **end for**

8: **end for**

9: Summarize each of the results

10: Select the best estimator

11: Fit the data to the best estimator

12: Evaluate the performance of model using various metrics

An array of classifiers used on the features extracted for classification to predict various upper limb movements are depicted in Fig. 10.

Various classification techniques have been used to differentiate various upper limb movements. The research focuses on algorithms such as NB, DT, KNN, SVM, RF, and VC. Before training the data on a classification model each of the data point is assigned a class number which describes which movement it belongs to. An NxN confusion matrix is plotted where N is the number of movements used to assess the performance of the model.

<figure><img src="image_12.png" /><figcaption>**Fig. 10.** An array of classifiers used for classifying various types of movements</figcaption></figure>
## 4 Results and Discussion

In this paper, EMG signals have been captured in the form of (.xlsx). All the actions including elbow extension, hand close, ulnar deviation, wrist extension, elbow flexion, hand open, radial deviation and wrist flexion were performed by all the 8 healthy subjects. After the data pre-processing, some of the values included 0 values or not a number (NAN) values that were either removed or exchanged by the mean of remaining values. The 8 electrodes i.e., 4 channels were placed on FCR, ECR, BB, and TB respectively. One excel file per action per subject was created for ease of understanding. The graph representing time and the channels was plotted.

All the samples were normalized keeping the sample rate as 1024 and duration of 15 s. Estimates were made for a number of time features, including VAR, IEMG, LD, RMS, AAC, DASDV, MAV, ZC, WAMP, MPR and WL. Different frequency features such as FR, MNP, TOT, MNF, MDF, PKF were also estimated.

<table><caption>**Table 2.** Accuracy achieved on different classifiers for the movement's elbow extension and elbow flexion</caption><thead><tr><th>Classifier</th><th>A1</th><th>A2</th><th>A3</th><th>A4</th></tr></thead><tbody><tr><td>Random Forest</td><td>73.35%</td><td>74.55%</td><td>58.05%</td><td>62.42%</td></tr><tr><td>Support Vector Machine</td><td>63.61%</td><td>67.19%</td><td>53.67%</td><td>72.16%</td></tr><tr><td>K-Nearest Neighbour</td><td>55.07%</td><td>67%</td><td>51.88%</td><td>58.64%</td></tr><tr><td>Decision Tree</td><td>71.37%</td><td>67.79%</td><td>59.64%</td><td>55.26%</td></tr><tr><td>Naive Bayes</td><td>65.40%</td><td>62.62%</td><td>47.51%</td><td>73.75%</td></tr><tr><td>Voting Classifier</td><td>70.97%</td><td>70.57%</td><td>52.08%</td><td>65.60%</td></tr></tbody></table>

In Table 2 and Fig. 11, A1 is the classification between elbow extension and elbow flexion, A2 is the classification between hand close and hand open, A3 is the classification between radial deviation and ulnar deviation and A4 is the classification between wrist extension and wrist flexion.

The accuracies of all the classifiers on the movements are depicted in Table 2. Total 6 different classifiers were implemented RF, KNN, SVM, NB, DT and a VC where equal weights were given to each of the classifiers. Movements mentioned in the methodology were categorized using an array of classifiers. Random forest classifier provided the highest accuracy equal to 73.35% for classifying elbow extension and elbow flexion movements and 74.55% for classifying hand open and hand close movements. Decision Tree provided the highest accuracy equal to 59.64% for classifying radial deviation and ulnar deviation movements. Naïve Bayes provided the highest accuracy equal to 73.75% for classifying wrist extension and wrist flexion movements.
<figure><img src="image_13.png" /><figcaption>Fig. 11. Classifiers with best performance</figcaption></figure>

**5 Conclusion**

In this paper, electromyography (EMG) was used to examine the flexor and extensor carpi radialis, as well as the biceps and triceps brachii. Various movements were categorized. Accuracy achieved is 73.75% on naïve bayes classifier to classify wrist extension and wrist flexion movements, 59.64% on decision tree to classify radial deviation and ulnar deviation movements, 74.55% and 73.35% on random forest to classify opening and closing of hand, and elbow extension and elbow flexion movements respectively. A total of 8 EMG signals are demonstrated corresponding to the upper limb. An improved control schema utilizing pattern recognition can be used to overcome the limitations of the system. This work can be extended further for the development of prosthetic arm.

**Acknowledgement.** We would like to extend our profound appreciation to the researchers, medical professionals, committee members, and study participants. The AICTE, the GOI, New Delhi, and VIT, Pune are grateful to have supported this research project financially (File No. 8-53/FDC/RPS (POLICY-I)/2019-20).

**References**

1. Samuel, O.W., et al.: A novel time-domain descriptor for improved prediction of upper limb movement intent in EMG-PR system. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3513–3516 (2018)

2. Lobov, S., Krilova, N., Kastalskiy, I., Kazantsev, V., Makarov, V.: Latent factors limiting the performance of sEMG-interfaces. Sensors **18**(4), 1122 (2018)

3. Guo, S., Pang, M., Gao, B., Hirata, H., Ishihara, H.: Comparison of sEMG-based feature extraction and motion classification methods for upper-limb movement. Sensors **15**(4), 9022–9038 (2015)
4. Burns, A., Adeli, H., Buford, J.A.: Upper limb movement classification via electromyographic signals and an enhanced probabilistic network. J. Med. Syst. 44(10), 1-12 (2020)

5. Buongiorno, D., Cascarano, G.D., Brunetti, A., De Feudis, I., Bevilacqua, V.: A survey on deep learning in electromyographic signal analysis. In: International Conference on Intelligent Computing 2019 Aug 3, pp. 751-761. Springer, Cham (2019)

6. Samuel, O.W., et al.: Pattern recognition of electromyography signals based on novel time domain features for amputees' limb motion classification. Comput. Electr. Eng. 67, 646-655 (2018)

7. Al-Timemy, H., Khushaba, R.N., Bugmann, G., Escudero, J.: Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans. Neural Syst. Rehabil. Eng. 24(6), 650-661 (2016)

8. Xia, P., Hu, J., Peng, Y.: EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks. Artif. Organs 42(5), E67-E77 (2018)

9. Antuvan, C.W., Bisio, F., Marini, F., Yen, S.C., Cambria, E., Masia, L.: Role of muscle synergies in real-time classification of upper limb motions using extreme learning machines. J. Neuroeng. Rehabil. 13(1), 1-15 (2016)

10. Pancholi, S., Joshi, A.M.: Portable EMG data acquisition module for upper limb prosthesis application. In: IEEE Sensors Journal, vol. 18, no. 8, pp. 3436-3443. 15 April (2018)

11. Wang, Y., Wu, Q., Dey, N., Fong, S., Ashour, A.S.: Deep back propagation-long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation. Biocybernetics Biomed. Eng. 40(3), 987-1001 (2020)

12. Samuel, O.W., et al.: Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: advances, current challenges, and future prospects. IEEE Access 7, 10150-10165 (2019)

13. Yang, D., Yang, W., Huang, Q., Liu, H.: Classification of multiple finger motions during dynamic upper limb movements. IEEE J. Biomed. Health Inform. 21(1), 134-141 (2017)

14. Mukhopadhyay, A.K., Samui, S.: An experimental study on upper limb position invariant EMG signal classification based on deep neural network. Biomed. Signal Process. Control 55, 101669 (2020)

15. Negi, S., Kumar, Y., Mishra, V.M.: Feature extraction and classification for EMG signals using linear discriminant analysis. In: 2016 2nd International Conference on Advances in Computing, Communication & Automation (Fall), pp. 1-6 (2016)

16. Li, X., Samuel, O.W., Zhang, X., Wang, H., Fang, P., Li, G.: A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees. J. Neuroeng. Rehabil. 14(1), 1-13 (2017)

17. Bi, L., Guan, C.: A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration. Biomed. Signal Process. Control 51(May), 113-127 (2019)

18. Trigili, E., et al.: Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks. J. Neuroengineering rehabil. 16(1), 1-16 (2019)

19. Gaudet, G., Raison, M., Achiche, S.: Classification of upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features. Eng. Appl. Artif. Intell. 68, 153-164 (2018)

20. Guo, W., Sheng, X., Liu, H., Zhu, X.: Toward an enhanced human-machine interface for upper-limb prosthesis control with combined EMG and NIRS signals. IEEE Trans. Human-Mach. Syst. 47(4), 564-575 (2017)
21. Pino, E.J., Arias, Y., Aqueveque, P.: Wearable EMG shirt for upper limb training. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Jul 18, pp. 4406–4409 (2018)

22. Costa, Á., Itkonen, M., Yamasaki, H., Alnajjar, F.S., Shimoda, S.: Importance of muscle selection for EMG signal analysis during upper limb rehabilitation of stroke patients. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2510–2513 (2017)