Table 1.
The EMG pattern recognition performances of different feature extraction methods.
| Parameter | Classifier | FS1 | FS2 | FS3 | FS4 | Proposed | |
|---|---|---|---|---|---|---|---|
| Dataset 1 | Accuracy | LDA | 96.79 ± 1.00 | 96.27 ± 0.88 | 96.21 ± 0.92 | 96.03 ± 1.05 | 97.79 ± 0.52 |
| SVM | 95.73 ± 0.93 | 95.14 ± 1.06 | 95.53 ± 0.93 | 95.66 ± 1.02 | 97.52 ± 0.56 | ||
| KNN | 94.94 ± 1.04 | 94.39 ± 1.12 | 94.95 ± 0.98 | 95.25 ± 1.15 | 97.23 ± 0.63 | ||
| Sensitivity | LDA | 83.96 ± 4.98 | 81.36 ± 4.38 | 81.03 ± 4.60 | 80.15 ± 5.25 | 88.97 ± 2.58 | |
| SVM | 78.65 ± 4.67 | 75.70 ± 5.30 | 77.63 ± 4.64 | 78.30 ± 5.08 | 87.62 ± 2.80 | ||
| KNN | 74.71 ± 5.19 | 71.94 ± 5.60 | 74.74 ± 4.90 | 76.24 ± 5.77 | 86.16 ± 3.13 | ||
| Specificity | LDA | 98.22 ± 0.55 | 97.93 ± 0.49 | 97.89 ± 0.51 | 97.79 ± 0.58 | 98.77 ± 0.29 | |
| SVM | 97.63 ± 0.52 | 97.30 ± 0.59 | 97.51 ± 0.52 | 97.59 ± 0.56 | 98.46 ± 0.35 | ||
| KNN | 97.19 ± 0.58 | 96.88 ± 0.62 | 97.19 ± 0.54 | 97.36 ± 0.64 | 98.62 ± 0.31 | ||
| Precision | LDA | 85.24 ± 4.72 | 82.41 ± 4.28 | 82.55 ± 4.49 | 81.41 ± 4.32 | 89.95 ± 2.53 | |
| SVM | 79.42 ± 5.00 | 77.11 ± 5.13 | 78.83 ± 4.81 | 79.61 ± 5.35 | 88.47 ± 2.77 | ||
| KNN | 75.96 ± 5.19 | 73.59 ± 5.40 | 76.27 ± 4.69 | 77.74 ± 5.58 | 87.03 ± 3.11 | ||
| F1 score | LDA | 83.49 ± 5.13 | 80.56 ± 4.57 | 80.58 ± 4.69 | 79.41 ± 5.59 | 88.55 ± 2.72 | |
| SVM | 78.08 ± 4.84 | 75.06 ± 5.41 | 76.97 ± 4.83 | 77.71 ± 5.32 | 87.26 ± 2.88 | ||
| KNN | 74.22 ± 5.26 | 71.38 ± 5.64 | 74.26 ± 4.94 | 75.74 ± 5.91 | 85.77 ± 3.24 | ||
|
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| Dataset 2 | Accuracy | LDA | 97.18 ± 0.94 | 96.73 ± 0.93 | 96.69 ± 1.07 | 95.78 ± 1.08 | 98.36 ± 0.45 |
| SVM | 96.58 ± 0.97 | 96.00 ± 0.85 | 95.88 ± 1.00 | 95.32 ± 1.29 | 98.07 ± 0.59 | ||
| KNN | 96.00 ± 1.08 | 95.54 ± 0.96 | 95.47 ± 1.24 | 94.85 ± 1.41 | 97.97 ± 0.66 | ||
| Sensitivity | LDA | 85.88 ± 4.72 | 83.66 ± 4.66 | 83.43 ± 5.34 | 78.88 ± 5.40 | 91.78 ± 2.25 | |
| SVM | 82.88 ± 4.86 | 80.01 ± 4.24 | 79.42 ± 4.99 | 76.60 ± 6.44 | 90.34 ± 2.96 | ||
| KNN | 80.06 ± 5.38 | 77.69 ± 4.80 | 77.34 ± 6.20 | 74.25 ± 7.04 | 89.83 ± 3.28 | ||
| Specificity | LDA | 98.43 ± 0.52 | 98.18 ± 0.52 | 98.16 ± 0.59 | 97.65 ± 0.60 | 99.09 ± 0.25 | |
| SVM | 98.10 ± 0.54 | 97.78 ± 0.47 | 97.71 ± 0.55 | 97.40 ± 0.72 | 98.93 ± 0.33 | ||
| KNN | 97.78 ± 0.60 | 97.52 ± 0.53 | 97.48 ± 0.69 | 97.14 ± 0.78 | 98.87 ± 0.36 | ||
| Precision | LDA | 87.02 ± 4.69 | 85.26 ± 4.31 | 84.79 ± 4.86 | 80.05 ± 4.90 | 92.65 ± 1.99 | |
| SVM | 83.92 ± 4.92 | 81.51 ± 4.05 | 80.75 ± 5.01 | 78.12 ± 6.58 | 91.20 ± 2.68 | ||
| KNN | 81.62 ± 5.41 | 79.11 ± 4.71 | 78.68 ± 6.11 | 75.36 ± 7.28 | 90.70 ± 2.95 | ||
| F1 score | LDA | 85.55 ± 4.84 | 83.22 ± 4.68 | 83.14 ± 5.34 | 78.37 ± 5.50 | 91.59 ± 2.29 | |
| SVM | 82.63 ± 5.01 | 79.78 ± 4.34 | 79.00 ± 5.21 | 76.17 ± 6.70 | 90.19 ± 3.02 | ||
| KNN | 79.87 ± 5.58 | 77.30 ± 5.12 | 77.00 ± 6.40 | 73.71 ± 7.33 | 89.70 ± 3.39 | ||