Research Article
Wrist EMG Monitoring Using Neural Networks Techniques
Table 6
Comparison of our system with relevant literature.
| System | Classification method | Employed features | Channels number | (%) Accuracy |
| Chu et al. [27] | PCA + self-organizing feature map | Wave package transform | 4 | 97.4 | Ahsan et al. [28] | BPNN | MAV, RMS, VAR, standard deviation, zero crossing, and waveform length. | 1 | 86.76 | Tohi et al. [29] | GANN | Fast fourier transform | 4 | 77.5 | Khezri and Jahed [30] | PCA + NN + FIS | MAV, slope sign changes, autoregressive model coefficients (time domain); discrete wavelet transform (frequency domain) | 2 | 82 | Liu et al. [15] | CKLM + SVM | coefficients autoregressive model and histogram of EMG | 3 | 93.54 | George et al. [31] | LDA + NN | mean, variance, skewness, and absolute value | 1 | 90 | Matsumura et al. [32] | PCA + NN | Fast Fourier transform | 4 | 72.86 | This work | MLP + BP | — | 2 | 95.69 |
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Abbreviations, principal components analysis (PCA), back-propagation neural network (BPNN), linear discriminant analysis (LDA), genetic algorithm combined with neural network (GANN), cascaded kernel learning machine (CKML), and support vector machine (SVM).
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