Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Proposed Feature Extraction Method
2.2. Description of EMG Dataset
2.3. EMG Pattern Recognition
2.4. EMG Pattern Recognition Performance with Training Strategies of Various Force Level
2.5. Statistical Test
2.6. RES Index
3. Results
3.1. Signal Observation
3.2. Impact of Nonlinear Transformation
3.3. The Impact of Window Length on Clustering Performance
3.4. Training and Testing the Classifiers with Same Force Level (Case 1)
3.5. Training the Classifiers with a Single Force Level at a Time and Testing the Classifiers with All Three Force Levels (Case 2)
3.6. Training the Classifiers with Any Two Force Levels at a Time and Testing the Classifiers with All Three Force Levels (Case 3)
3.7. Training the Classifiers with all Three Force Levels and Testing the Classifiers with All Three Force Levels (Case 4)
3.8. Computational Time and Memory Size
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. The EMG Pattern Recognition Performance for Different Training and Testing Cases
Parameter | Classifier | Proposed | TSD | TDPSD | Wavelet | TDF | AR-RMS | TD | |
---|---|---|---|---|---|---|---|---|---|
Training and testing with low force | Accuracy | LDA | 97.86 ± 1.59 | 97.20 ± 1.65 | 96.97 ± 2.50 | 96.49 ± 2.73 | 95.88 ± 2.71 | 95.81 ± 2.69 | 95.17 ± 3.10 |
SVM | 97.93 ± 1.70 | 97.36 ± 1.67 | 97.06 ± 2.57 | 96.41 ± 2.91 | 95.88 ± 2.83 | 95.80 ± 2.88 | 95.11 ± 3.19 | ||
KNN | 97.78 ± 1.78 | 97.26 ± 1.86 | 96.87 ± 2.81 | 96.13 ± 3.13 | 95.55 ± 3.00 | 95.27 ± 3.28 | 94.69 ± 3.52 | ||
Sensitivity | LDA | 93.57 ± 4.77 | 91.60 ± 4.96 | 90.91 ± 7.50 | 89.46 ± 8.20 | 87.65 ± 8.14 | 87.43 ± 8.06 | 85.52 ± 9.31 | |
SVM | 93.80 ± 5.09 | 92.09 ± 5.01 | 91.19 ± 7.72 | 89.22 ± 8.74 | 87.64 ± 8.48 | 87.39 ± 8.65 | 85.34 ± 9.56 | ||
KNN | 93.34 ± 5.35 | 91.79 ± 5.59 | 90.60 ± 8.44 | 88.40 ± 9.39 | 86.64 ± 8.99 | 85.80 ± 9.84 | 84.06 ± 10.55 | ||
Specificity | LDA | 98.71 ± 0.92 | 98.32 ± 0.96 | 98.21 ± 1.45 | 97.90 ± 1.60 | 97.56 ± 1.50 | 97.47 ± 1.59 | 97.14 ± 1.74 | |
SVM | 98.75 ± 1.00 | 98.42 ± 0.98 | 98.27 ± 1.52 | 97.86 ± 1.72 | 97.56 ± 1.62 | 97.46 ± 1.74 | 97.12 ± 1.79 | ||
KNN | 98.65 ± 1.08 | 98.33 ± 1.12 | 98.12 ± 1.71 | 97.66 ± 1.89 | 97.32 ± 1.78 | 97.11 ± 2.04 | 96.82 ± 2.06 | ||
Precision | LDA | 94.48 ± 4.18 | 92.47 ± 4.42 | 91.86 ± 6.83 | 90.52 ± 7.58 | 88.95 ± 7.33 | 88.81 ± 7.62 | 87.07 ± 8.48 | |
SVM | 94.46 ± 4.41 | 93.03 ± 4.62 | 92.09 ± 7.04 | 90.12 ± 8.14 | 88.83 ± 7.85 | 88.56 ± 8.08 | 86.91 ± 8.73 | ||
KNN | 93.95 ± 4.78 | 92.68 ± 5.17 | 91.43 ± 7.97 | 89.29 ± 8.89 | 87.69 ± 8.60 | 86.91 ± 9.54 | 85.47 ± 10.29 | ||
F1 Score | LDA | 93.30 ± 4.91 | 91.28 ± 5.06 | 90.69 ± 7.65 | 89.29 ± 8.32 | 87.39 ± 8.15 | 87.14 ± 8.07 | 85.01 ± 9.51 | |
SVM | 93.64 ± 5.18 | 91.89 ± 5.05 | 91.05 ± 7.87 | 89.07 ± 8.85 | 87.42 ± 8.52 | 87.25 ± 8.64 | 85.01 ± 9.76 | ||
KNN | 93.22 ± 5.46 | 91.60 ± 5.69 | 90.44 ± 8.72 | 88.24 ± 9.55 | 86.39 ± 9.10 | 85.55 ± 10.0 | 83.67 ± 10.86 | ||
Training and testing with medium force | Accuracy | LDA | 97.89 ± 1.05 | 97.30 ± 1.03 | 96.75 ± 1.68 | 96.21 ± 1.80 | 95.91 ± 1.79 | 96.00 ± 1.92 | 95.13 ± 2.21 |
SVM | 97.91 ± 1.09 | 97.33 ± 1.06 | 96.96 ± 1.68 | 96.12 ± 1.85 | 95.95 ± 1.8 | 95.85 ± 1.98 | 95.10 ± 2.25 | ||
KNN | 97.65 ± 1.20 | 97.17 ± 1.15 | 96.56 ± 1.87 | 95.75 ± 2.09 | 95.53 ± 2.17 | 95.53 ± 2.29 | 94.55 ± 2.52 | ||
Sensitivity | LDA | 93.66 ± 3.16 | 91.90 ± 3.09 | 90.25 ± 5.03 | 88.62 ± 5.40 | 87.72 ± 5.38 | 88.00 ± 5.77 | 85.40 ± 6.62 | |
SVM | 93.72 ± 3.27 | 91.99 ± 3.19 | 90.87 ± 5.05 | 88.36 ± 5.55 | 87.84 ± 5.40 | 87.55 ± 5.94 | 85.31 ± 6.76 | ||
KNN | 92.96 ± 3.61 | 91.52 ± 3.45 | 89.68 ± 5.61 | 87.24 ± 6.28 | 86.59 ± 6.50 | 86.58 ± 6.87 | 83.66 ± 7.57 | ||
Specificity | LDA | 98.82 ± 0.64 | 98.50 ± 0.61 | 98.17 ± 0.99 | 97.82 ± 1.09 | 97.62 ± 1.08 | 97.71 ± 1.13 | 97.18 ± 1.32 | |
SVM | 98.81 ± 0.65 | 98.49 ± 0.63 | 98.29 ± 0.99 | 97.76 ± 1.12 | 97.61 ± 1.10 | 97.61 ± 1.16 | 97.14 ± 1.36 | ||
KNN | 98.66 ± 0.72 | 98.41 ± 0.69 | 98.05 ± 1.09 | 97.53 ± 1.26 | 97.36 ± 1.32 | 97.42 ± 1.36 | 96.79 ± 1.53 | ||
Precision | LDA | 94.25 ± 3.16 | 92.87 ± 3.08 | 91.24 ± 4.97 | 89.64 ± 5.43 | 88.88 ± 5.28 | 88.83 ± 5.76 | 86.77 ± 6.29 | |
SVM | 94.17 ± 3.21 | 92.75 ± 3.16 | 91.62 ± 4.96 | 89.33 ± 5.55 | 88.86 ± 5.35 | 88.49 ± 5.88 | 86.68 ± 6.57 | ||
KNN | 93.45 ± 3.52 | 92.26 ± 3.45 | 90.51 ± 5.52 | 88.08 ± 6.26 | 87.57 ± 6.39 | 87.38 ± 6.90 | 84.97 ± 7.40 | ||
F1 Score | LDA | 93.55 ± 3.21 | 91.70 ± 3.22 | 90.11 ± 5.07 | 88.51 ± 5.45 | 87.49 ± 5.42 | 87.87 ± 5.83 | 85.12 ± 6.69 | |
SVM | 93.62 ± 3.31 | 91.85 ± 3.33 | 90.81 ± 5.08 | 88.23 ± 5.62 | 87.65 ± 5.46 | 87.44 ± 5.96 | 85.12 ± 6.82 | ||
KNN | 92.84 ± 3.67 | 91.39 ± 3.59 | 89.58 ± 5.67 | 87.07 ± 6.37 | 86.35 ± 6.57 | 86.44 ± 6.99 | 83.39 ± 7.65 | ||
Training and testing with high force | Accuracy | LDA | 97.44 ± 1.10 | 96.69 ± 1.60 | 96.34 ± 1.75 | 95.56 ± 1.70 | 95.40 ± 1.98 | 95.69 ± 1.50 | 94.89 ± 1.88 |
SVM | 97.32 ± 1.22 | 96.63 ± 1.67 | 96.32 ± 1.70 | 95.47 ± 1.77 | 95.36 ± 1.94 | 95.52 ± 1.50 | 94.89 ± 1.99 | ||
KNN | 97.13 ± 1.25 | 96.44 ± 1.86 | 95.95 ± 1.90 | 95.10 ± 1.85 | 94.99 ± 2.16 | 95.07 ± 1.75 | 94.39 ± 2.17 | ||
Sensitivity | LDA | 92.33 ± 3.30 | 90.07 ± 4.80 | 89.01 ± 5.24 | 86.69 ± 5.09 | 86.20 ± 5.94 | 87.07 ± 4.50 | 84.68 ± 5.63 | |
SVM | 91.97 ± 3.66 | 89.90 ± 5.01 | 88.96 ± 5.09 | 86.41 ± 5.31 | 86.08 ± 5.81 | 86.57 ± 4.50 | 84.68 ± 5.97 | ||
KNN | 91.38 ± 3.75 | 89.31 ± 5.57 | 87.84 ± 5.69 | 85.30 ± 5.55 | 84.97 ± 6.49 | 85.21 ± 5.24 | 83.18 ± 6.51 | ||
Specificity | LDA | 98.54 ± 0.65 | 98.11 ± 0.91 | 97.90 ± 1.01 | 97.43 ± 0.99 | 97.33 ± 1.17 | 97.52 ± 0.92 | 97.06 ± 1.14 | |
SVM | 98.48 ± 0.71 | 98.07 ± 0.97 | 97.90 ± 0.99 | 97.38 ± 1.03 | 97.31 ± 1.15 | 97.41 ± 0.92 | 97.07 ± 1.20 | ||
KNN | 98.36 ± 0.72 | 97.94 ± 1.09 | 97.67 ± 1.12 | 97.14 ± 1.10 | 97.05 ± 1.32 | 97.13 ± 1.09 | 96.73 ± 1.33 | ||
Precision | LDA | 92.97 ± 3.11 | 90.97 ± 4.29 | 90.11 ± 4.21 | 87.74 ± 4.73 | 87.35 ± 5.15 | 87.98 ± 4.38 | 86.06 ± 4.90 | |
SVM | 92.79 ± 3.52 | 90.79 ± 4.61 | 90.03 ± 4.13 | 87.51 ± 4.93 | 87.28 ± 5.06 | 87.46 ± 4.26 | 86.01 ± 5.36 | ||
KNN | 92.24 ± 3.52 | 90.24 ± 5.11 | 88.93 ± 4.80 | 86.31 ± 5.25 | 86.09 ± 6.01 | 86.25 ± 5.06 | 84.48 ± 5.95 | ||
F1 Score | LDA | 92.07 ± 3.40 | 89.77 ± 4.87 | 88.67 ± 5.12 | 86.37 ± 5.03 | 85.78 ± 5.87 | 86.75 ± 4.45 | 84.23 ± 5.53 | |
SVM | 91.67 ± 3.78 | 89.58 ± 5.08 | 88.61 ± 4.94 | 86.13 ± 5.28 | 85.70 ± 5.80 | 86.23 ± 4.41 | 84.26 ± 5.94 | ||
KNN | 91.09 ± 3.84 | 88.96 ± 5.72 | 87.47 ± 5.61 | 84.94 ± 5.54 | 84.61 ± 6.46 | 84.89 ± 5.16 | 82.74 ± 6.53 |
Parameter | Classifier | Proposed | TSD | TDPSD | Wavelet | TDF | AR-RMS | TD | |
---|---|---|---|---|---|---|---|---|---|
Training with low force | Accuracy | LDA | 89.07 ± 3.15 | 88.04 ± 2.61 | 88.48 ± 2.78 | 87.63 ± 2.91 | 86.52 ± 3.04 | 87.23 ± 2.71 | 86.18 ± 2.92 |
SVM | 89.10 ± 2.79 | 88.01 ± 2.57 | 88.57 ± 2.73 | 87.70 ± 2.83 | 86.46 ± 3.00 | 87.28 ± 2.94 | 86.15 ± 2.87 | ||
KNN | 89.02 ± 2.82 | 88.06 ± 2.64 | 88.55 ± 2.64 | 87.57 ± 2.86 | 86.84 ± 2.82 | 87.27 ± 2.84 | 86.50 ± 2.70 | ||
Sensitivity | LDA | 67.22 ± 9.44 | 64.13 ± 7.84 | 65.43 ± 8.33 | 62.90 ± 8.74 | 59.57 ± 9.13 | 61.69 ± 8.12 | 58.55 ± 8.76 | |
SVM | 67.29 ± 8.38 | 64.03 ± 7.71 | 65.72 ± 8.19 | 63.11 ± 8.49 | 59.37 ± 8.99 | 61.83 ± 8.82 | 58.46 ± 8.60 | ||
KNN | 67.07 ± 8.45 | 64.17 ± 7.91 | 65.64 ± 7.92 | 62.72 ± 8.58 | 60.53 ± 8.45 | 61.81 ± 8.51 | 59.50 ± 8.11 | ||
Specificity | LDA | 93.72 ± 1.75 | 93.11 ± 1.49 | 93.34 ± 1.59 | 92.87 ± 1.68 | 92.17 ± 1.77 | 92.59 ± 1.58 | 92.00 ± 1.69 | |
SVM | 93.70 ± 1.64 | 93.11 ± 1.47 | 93.4 ± 1.53 | 92.91 ± 1.71 | 92.07 ± 1.85 | 92.62 ± 1.75 | 91.87 ± 1.75 | ||
KNN | 93.65 ± 1.65 | 93.12 ± 1.54 | 93.36 ± 1.5 | 92.82 ± 1.69 | 92.30 ± 1.70 | 92.59 ± 1.76 | 92.11 ± 1.69 | ||
Precision | LDA | 75.51 ± 5.90 | 72.35 ± 4.97 | 73.37 ± 7.31 | 70.74 ± 6.72 | 67.70 ± 7.93 | 69.21 ± 6.65 | 66.75 ± 7.87 | |
SVM | 74.63 ± 5.92 | 72.21 ± 5.51 | 73.21 ± 6.62 | 70.49 ± 6.57 | 67.55 ± 7.98 | 69.06 ± 7.45 | 66.55 ± 8.55 | ||
KNN | 74.21 ± 5.94 | 72.06 ± 5.85 | 72.81 ± 6.96 | 69.84 ± 7.07 | 66.83 ± 7.42 | 67.49 ± 7.71 | 65.42 ± 8.22 | ||
F1 Score | LDA | 67.04 ± 9.07 | 64.00 ± 7.31 | 65.10 ± 7.95 | 62.58 ± 8.48 | 59.23 ± 8.76 | 61.11 ± 7.90 | 58.05 ± 8.48 | |
SVM | 67.22 ± 8.12 | 64.02 ± 7.17 | 65.40 ± 8.05 | 62.85 ± 8.21 | 59.40 ± 8.49 | 61.52 ± 8.54 | 58.30 ± 8.30 | ||
KNN | 67.07 ± 8.19 | 64.22 ± 7.41 | 65.35 ± 7.86 | 62.47 ± 8.39 | 60.16 ± 8.14 | 61.31 ± 8.31 | 58.96 ± 8.11 | ||
Training with medium force | Accuracy | LDA | 91.99 ± 2.35 | 90.86 ± 2.05 | 90.66 ± 2.63 | 90.57 ± 2.41 | 89.20 ± 2.97 | 90.03 ± 2.40 | 88.96 ± 3.03 |
SVM | 91.94 ± 2.44 | 90.82 ± 2.07 | 90.78 ± 2.56 | 90.45 ± 2.39 | 89.26 ± 2.82 | 89.91 ± 2.49 | 88.78 ± 2.90 | ||
KNN | 91.89 ± 2.42 | 90.86 ± 1.92 | 90.76 ± 2.67 | 90.27 ± 2.56 | 89.22 ± 3.06 | 89.81 ± 2.65 | 88.76 ± 2.92 | ||
Sensitivity | LDA | 75.97 ± 7.06 | 72.58 ± 6.14 | 71.97 ± 7.88 | 71.70 ± 7.22 | 67.61 ± 8.91 | 70.10 ± 7.20 | 66.89 ± 9.09 | |
SVM | 75.81 ± 7.33 | 72.46 ± 6.21 | 72.35 ± 7.68 | 71.34 ± 7.17 | 67.79 ± 8.47 | 69.74 ± 7.46 | 66.34 ± 8.70 | ||
KNN | 75.67 ± 7.26 | 72.57 ± 5.77 | 72.27 ± 8.00 | 70.80 ± 7.68 | 67.67 ± 9.19 | 69.44 ± 7.95 | 66.29 ± 8.76 | ||
Specificity | LDA | 95.29 ± 1.44 | 94.61 ± 1.30 | 94.49 ± 1.59 | 94.41 ± 1.49 | 93.55 ± 1.81 | 94.11 ± 1.44 | 93.38 ± 1.85 | |
SVM | 95.24 ± 1.50 | 94.57 ± 1.31 | 94.54 ± 1.57 | 94.34 ± 1.49 | 93.56 ± 1.74 | 94.02 ± 1.50 | 93.26 ± 1.78 | ||
KNN | 95.21 ± 1.50 | 94.58 ± 1.23 | 94.52 ± 1.65 | 94.22 ± 1.61 | 93.52 ± 1.91 | 93.95 ± 1.62 | 93.23 ± 1.83 | ||
Precision | LDA | 78.70 ± 5.93 | 75.77 ± 4.91 | 75.12 ± 6.73 | 74.03 ± 6.54 | 70.41 ± 8.26 | 72.37 ± 7.12 | 69.18 ± 8.73 | |
SVM | 78.57 ± 6.02 | 75.71 ± 4.76 | 75.05 ± 6.94 | 73.45 ± 6.49 | 70.63 ± 7.90 | 72.03 ± 7.17 | 69.03 ± 8.24 | ||
KNN | 78.27 ± 6.10 | 75.55 ± 4.51 | 74.69 ± 7.39 | 72.77 ± 7.06 | 70.11 ± 8.51 | 71.31 ± 8.17 | 68.33 ± 8.51 | ||
F1 Score | LDA | 75.83 ± 6.89 | 72.47 ± 5.94 | 71.75 ± 7.53 | 71.44 ± 6.92 | 67.42 ± 8.68 | 69.94 ± 7.08 | 66.64 ± 8.90 | |
SVM | 75.76 ± 7.08 | 72.46 ± 5.85 | 72.13 ± 7.55 | 71.09 ± 6.91 | 67.61 ± 8.18 | 69.61 ± 7.34 | 66.09 ± 8.46 | ||
KNN | 75.61 ± 7.05 | 72.54 ± 5.48 | 72.08 ± 7.78 | 70.53 ± 7.47 | 67.52 ± 8.95 | 69.20 ± 7.98 | 66.01 ± 8.70 | ||
Training with high force | Accuracy | LDA | 89.93 ± 2.26 | 88.76 ± 2.21 | 88.31 ± 2.70 | 88.11 ± 2.29 | 87.20 ± 2.47 | 87.92 ± 2.65 | 86.24 ± 2.90 |
SVM | 89.72 ± 2.11 | 88.65 ± 2.03 | 88.21 ± 2.61 | 88.04 ± 2.10 | 87.32 ± 2.47 | 87.76 ± 2.44 | 86.40 ± 2.73 | ||
KNN | 89.53 ± 2.38 | 88.47 ± 2.16 | 87.95 ± 2.76 | 87.55 ± 2.18 | 86.76 ± 2.55 | 87.19 ± 2.46 | 85.80 ± 2.63 | ||
Sensitivity | LDA | 69.79 ± 6.79 | 66.28 ± 6.64 | 64.93 ± 8.09 | 64.32 ± 6.88 | 61.60 ± 7.42 | 63.76 ± 7.94 | 58.73 ± 8.70 | |
SVM | 69.17 ± 6.33 | 65.94 ± 6.09 | 64.64 ± 7.84 | 64.12 ± 6.29 | 61.95 ± 7.42 | 63.28 ± 7.32 | 59.21 ± 8.19 | ||
KNN | 68.60 ± 7.13 | 65.42 ± 6.47 | 63.84 ± 8.27 | 62.64 ± 6.55 | 60.29 ± 7.66 | 61.57 ± 7.39 | 57.40 ± 7.88 | ||
Specificity | LDA | 93.90 ± 1.32 | 93.18 ± 1.29 | 92.85 ± 1.63 | 92.75 ± 1.34 | 92.11 ± 1.57 | 92.61 ± 1.57 | 91.56 ± 1.69 | |
SVM | 93.77 ± 1.23 | 93.10 ± 1.20 | 92.76 ± 1.57 | 92.67 ± 1.28 | 92.12 ± 1.58 | 92.50 ± 1.46 | 91.59 ± 1.65 | ||
KNN | 93.63 ± 1.41 | 92.97 ± 1.31 | 92.54 ± 1.68 | 92.35 ± 1.32 | 91.78 ± 1.66 | 92.12 ± 1.49 | 91.22 ± 1.60 | ||
Precision | LDA | 74.84 ± 5.30 | 72.18 ± 4.89 | 70.51 ± 7.23 | 69.12 ± 6.66 | 66.68 ± 7.46 | 68.12 ± 7.14 | 63.70 ± 8.82 | |
SVM | 74.36 ± 4.89 | 71.89 ± 4.11 | 70.85 ± 6.47 | 68.84 ± 6.32 | 66.73 ± 7.20 | 68.17 ± 6.58 | 65.00 ± 8.29 | ||
KNN | 73.82 ± 5.34 | 71.18 ± 5.10 | 70.60 ± 7.21 | 67.79 ± 6.46 | 66.36 ± 8.11 | 66.38 ± 7.18 | 63.85 ± 8.82 | ||
F1 Score | LDA | 69.10 ± 6.63 | 65.67 ± 6.42 | 63.63 ± 8.51 | 63.35 ± 7.15 | 60.46 ± 7.70 | 62.99 ± 8.17 | 57.69 ± 8.79 | |
SVM | 68.43 ± 5.93 | 65.27 ± 5.57 | 63.43 ± 8.12 | 63.16 ± 6.51 | 60.99 ± 7.65 | 62.58 ± 7.43 | 58.42 ± 8.30 | ||
KNN | 68.05 ± 6.80 | 64.86 ± 6.21 | 62.72 ± 8.54 | 61.80 ± 6.84 | 59.23 ± 7.91 | 60.77 ± 7.65 | 56.60 ± 8.07 |
Parameter | Classifier | Proposed | TSD | TDPSD | Wavelet | TDF | AR-RMS | TD | |
---|---|---|---|---|---|---|---|---|---|
Training with low and medium forces | Accuracy | LDA | 94.21 ± 1.83 | 93.30 ± 1.66 | 93.06 ± 2.34 | 92.80 ± 2.23 | 91.53 ± 2.69 | 92.06 ± 1.86 | 91.05 ± 2.52 |
SVM | 94.20 ± 1.84 | 93.22 ± 1.63 | 93.12 ± 2.32 | 92.75 ± 2.26 | 91.76 ± 2.59 | 92.02 ± 2.16 | 91.12 ± 2.82 | ||
KNN | 93.90 ± 1.97 | 93.03 ± 1.74 | 92.85 ± 2.47 | 92.37 ± 2.46 | 91.49 ± 2.81 | 91.72 ± 2.28 | 90.86 ± 2.90 | ||
Sensitivity | LDA | 82.64 ± 5.50 | 79.90 ± 4.97 | 79.18 ± 7.03 | 78.40 ± 6.70 | 74.60 ± 8.07 | 76.18 ± 5.59 | 73.14 ± 7.57 | |
SVM | 82.60 ± 5.52 | 79.66 ± 4.89 | 79.37 ± 6.95 | 78.24 ± 6.79 | 75.28 ± 7.76 | 76.06 ± 6.48 | 73.36 ± 8.45 | ||
KNN | 81.70 ± 5.90 | 79.09 ± 5.22 | 78.54 ± 7.42 | 77.11 ± 7.38 | 74.47 ± 8.44 | 75.15 ± 6.85 | 72.57 ± 8.69 | ||
Specificity | LDA | 96.64 ± 1.05 | 96.07 ± 0.95 | 95.95 ± 1.37 | 95.79 ± 1.31 | 95.00 ± 1.60 | 95.32 ± 1.11 | 94.68 ± 1.50 | |
SVM | 96.63 ± 1.06 | 96.03 ± 0.92 | 95.97 ± 1.35 | 95.75 ± 1.34 | 95.12 ± 1.52 | 95.30 ± 1.26 | 94.72 ± 1.67 | ||
KNN | 96.44 ± 1.14 | 95.90 ± 1.01 | 95.80 ± 1.46 | 95.51 ± 1.48 | 94.93 ± 1.71 | 95.09 ± 1.39 | 94.53 ± 1.76 | ||
Precision | LDA | 84.45 ± 4.98 | 81.95 ± 4.49 | 81.28 ± 6.79 | 80.19 ± 6.37 | 76.89 ± 7.89 | 77.98 ± 5.72 | 75.26 ± 7.81 | |
SVM | 84.37 ± 4.96 | 81.8 ± 4.27 | 81.28 ± 6.70 | 80.03 ± 6.43 | 77.24 ± 7.44 | 77.90 ± 6.18 | 75.55 ± 8.14 | ||
KNN | 83.29 ± 5.59 | 80.98 ± 4.93 | 80.24 ± 7.34 | 78.53 ± 7.41 | 75.91 ± 8.58 | 76.34 ± 7.18 | 73.94 ± 9.00 | ||
F1 Score | LDA | 82.63 ± 5.40 | 79.91 ± 4.92 | 79.13 ± 6.96 | 78.30 ± 6.59 | 74.64 ± 7.85 | 76.11 ± 5.58 | 73.04 ± 7.57 | |
SVM | 82.60 ± 5.39 | 79.72 ± 4.75 | 79.36 ± 6.85 | 78.21 ± 6.65 | 75.26 ± 7.62 | 76.05 ± 6.36 | 73.34 ± 8.35 | ||
KNN | 81.69 ± 5.83 | 79.10 ± 5.16 | 78.49 ± 7.37 | 77.02 ± 7.35 | 74.38 ± 8.38 | 75.02 ± 6.90 | 72.32 ± 8.85 | ||
Training with low and high forces | Accuracy | LDA | 95.34 ± 1.70 | 94.80 ± 1.61 | 94.17 ± 2.12 | 93.39 ± 2.12 | 92.55 ± 2.64 | 92.81 ± 2.24 | 91.57 ± 2.86 |
SVM | 95.37 ± 1.78 | 94.80 ± 1.70 | 94.25 ± 2.16 | 93.30 ± 2.17 | 92.49 ± 2.74 | 92.77 ± 2.35 | 91.60 ± 2.89 | ||
KNN | 94.98 ± 1.92 | 94.41 ± 1.87 | 93.72 ± 2.40 | 92.58 ± 2.48 | 91.77 ± 2.95 | 91.92 ± 2.60 | 90.73 ± 3.16 | ||
Sensitivity | LDA | 86.03 ± 5.10 | 84.41 ± 4.82 | 82.52 ± 6.35 | 80.18 ± 6.35 | 77.66 ± 7.93 | 78.43 ± 6.71 | 74.72 ± 8.58 | |
SVM | 86.12 ± 5.34 | 84.39 ± 5.11 | 82.74 ± 6.49 | 79.89 ± 6.52 | 77.47 ± 8.23 | 78.31 ± 7.06 | 74.81 ± 8.68 | ||
KNN | 84.94 ± 5.75 | 83.23 ± 5.60 | 81.16 ± 7.21 | 77.74 ± 7.43 | 75.32 ± 8.84 | 75.75 ± 7.79 | 72.20 ± 9.47 | ||
Specificity | LDA | 97.25 ± 1.00 | 96.90 ± 0.95 | 96.55 ± 1.24 | 96.07 ± 1.24 | 95.53 ± 1.61 | 95.67 ± 1.34 | 94.94 ± 1.71 | |
SVM | 97.26 ± 1.07 | 96.90 ± 1.02 | 96.58 ± 1.26 | 96.01 ± 1.29 | 95.49 ± 1.68 | 95.65 ± 1.42 | 94.97 ± 1.74 | ||
KNN | 97.02 ± 1.16 | 96.66 ± 1.12 | 96.25 ± 1.42 | 95.57 ± 1.50 | 95.03 ± 1.83 | 95.12 ± 1.59 | 94.41 ± 1.93 | ||
Precision | LDA | 86.81 ± 5.11 | 85.22 ± 4.78 | 83.61 ± 6.29 | 81.16 ± 6.36 | 78.61 ± 8.04 | 79.39 ± 6.60 | 76.00 ± 8.62 | |
SVM | 86.92 ± 5.37 | 85.26 ± 5.03 | 83.85 ± 6.31 | 80.84 ± 6.55 | 78.54 ± 8.25 | 79.33 ± 6.91 | 76.15 ± 8.63 | ||
KNN | 85.69 ± 5.82 | 84.00 ± 5.73 | 82.11 ± 7.18 | 78.63 ± 7.55 | 76.30 ± 9.08 | 76.61 ± 7.81 | 73.37 ± 9.63 | ||
F1 Score | LDA | 85.92 ± 5.17 | 84.27 ± 4.90 | 82.33 ± 6.43 | 80.06 ± 6.39 | 77.49 ± 8.02 | 78.31 ± 6.75 | 74.51 ± 8.74 | |
SVM | 86.05 ± 5.38 | 84.31 ± 5.15 | 82.62 ± 6.52 | 79.82 ± 6.53 | 77.43 ± 8.23 | 78.27 ± 7.06 | 74.79 ± 8.72 | ||
KNN | 84.84 ± 5.84 | 83.11 ± 5.72 | 81.02 ± 7.26 | 77.63 ± 7.52 | 75.21 ± 8.95 | 75.64 ± 7.89 | 72.08 ± 9.63 | ||
Training with medium and high forces | Accuracy | LDA | 94.27 ± 1.83 | 93.20 ± 1.54 | 93.00 ± 2.23 | 92.57 ± 2.24 | 91.71 ± 2.87 | 92.06 ± 2.29 | 90.82 ± 2.78 |
SVM | 94.18 ± 1.86 | 93.24 ± 1.49 | 93.16 ± 2.30 | 92.45 ± 2.29 | 91.95 ± 2.88 | 91.92 ± 2.29 | 91.11 ± 2.72 | ||
KNN | 93.88 ± 1.91 | 92.95 ± 1.60 | 92.82 ± 2.34 | 91.97 ± 2.43 | 91.24 ± 3.06 | 91.23 ± 2.45 | 90.29 ± 2.93 | ||
Sensitivity | LDA | 82.81 ± 5.50 | 79.60 ± 4.61 | 79.01 ± 6.70 | 77.72 ± 6.71 | 75.14 ± 8.62 | 76.18 ± 6.86 | 72.46 ± 8.33 | |
SVM | 82.53 ± 5.58 | 79.71 ± 4.48 | 79.49 ± 6.91 | 77.34 ± 6.87 | 75.86 ± 8.64 | 75.75 ± 6.87 | 73.33 ± 8.15 | ||
KNN | 81.64 ± 5.72 | 78.86 ± 4.80 | 78.46 ± 7.01 | 75.91 ± 7.30 | 73.71 ± 9.18 | 73.68 ± 7.35 | 70.87 ± 8.78 | ||
Specificity | LDA | 96.62 ± 1.09 | 95.95 ± 0.94 | 95.83 ± 1.33 | 95.55 ± 1.38 | 95.00 ± 1.80 | 95.24 ± 1.37 | 94.44 ± 1.72 | |
SVM | 96.55 ± 1.11 | 95.97 ± 0.91 | 95.92 ± 1.39 | 95.48 ± 1.41 | 95.13 ± 1.79 | 95.16 ± 1.38 | 94.61 ± 1.69 | ||
KNN | 96.37 ± 1.15 | 95.79 ± 0.99 | 95.70 ± 1.42 | 95.18 ± 1.50 | 94.68 ± 1.93 | 94.71 ± 1.49 | 94.09 ± 1.83 | ||
Precision | LDA | 84.54 ± 4.81 | 81.72 ± 3.94 | 80.95 ± 6.42 | 79.06 ± 6.72 | 76.90 ± 8.29 | 77.50 ± 6.83 | 74.29 ± 8.25 | |
SVM | 84.38 ± 4.70 | 81.79 ± 3.66 | 81.37 ± 6.51 | 78.77 ± 6.64 | 77.42 ± 8.33 | 77.17 ± 6.82 | 74.92 ± 8.24 | ||
KNN | 83.43 ± 4.94 | 80.95 ± 4.05 | 80.12 ± 6.81 | 77.19 ± 7.17 | 75.29 ± 9.02 | 74.93 ± 7.62 | 72.51 ± 8.95 | ||
F1 Score | LDA | 82.65 ± 5.47 | 79.42 ± 4.58 | 78.65 ± 6.85 | 77.41 ± 6.85 | 74.71 ± 8.75 | 75.86 ± 7.18 | 72.07 ± 8.49 | |
SVM | 82.40 ± 5.50 | 79.57 ± 4.41 | 79.18 ± 6.99 | 77.10 ± 6.92 | 75.49 ± 8.72 | 75.48 ± 7.09 | 72.96 ± 8.36 | ||
KNN | 81.54 ± 5.61 | 78.71 ± 4.77 | 78.10 ± 7.13 | 75.64 ± 7.37 | 73.36 ± 9.32 | 73.34 ± 7.66 | 70.47 ± 9.07 |
Parameter | Classifier | Proposed | TSD | TDPSD | Wavelet | TDF | AR-RMS | TD |
---|---|---|---|---|---|---|---|---|
Accuracy | LDA | 96.30 ± 1.52 | 95.75 ± 1.38 | 95.28 ± 1.94 | 94.45 ± 2.03 | 93.69 ± 2.60 | 93.89 ± 2.02 | 92.71 ± 2.74 |
SVM | 96.37 ± 1.60 | 95.80 ± 1.42 | 95.34 ± 2.03 | 94.37 ± 2.14 | 93.78 ± 2.62 | 93.98 ± 2.09 | 92.91 ± 2.77 | |
KNN | 95.88 ± 1.77 | 95.33 ± 1.68 | 94.79 ± 2.28 | 93.62 ± 2.51 | 92.95 ± 2.99 | 93.07 ± 2.48 | 91.88 ± 3.10 | |
Sensitivity | LDA | 88.89 ± 4.55 | 87.24 ± 4.13 | 85.83 ± 5.81 | 83.34 ± 6.10 | 81.07 ± 7.81 | 81.68 ± 6.06 | 78.12 ± 8.22 |
SVM | 89.11 ± 4.80 | 87.41 ± 4.25 | 86.02 ± 6.09 | 83.10 ± 6.43 | 81.35 ± 7.87 | 81.93 ± 6.28 | 78.72 ± 8.30 | |
KNN | 87.63 ± 5.32 | 86.00 ± 5.04 | 84.36 ± 6.85 | 80.86 ± 7.52 | 78.84 ± 8.96 | 79.21 ± 7.43 | 75.65 ± 9.31 | |
Specificity | LDA | 97.82 ± 0.90 | 97.51 ± 0.81 | 97.22 ± 1.14 | 96.71 ± 1.23 | 96.24 ± 1.60 | 96.36 ± 1.22 | 95.63 ± 1.67 |
SVM | 97.86 ± 0.95 | 97.53 ± 0.83 | 97.26 ± 1.20 | 96.66 ± 1.29 | 96.29 ± 1.59 | 96.41 ± 1.27 | 95.77 ± 1.67 | |
KNN | 97.56 ± 1.07 | 97.24 ± 1.01 | 96.91 ± 1.35 | 96.19 ± 1.54 | 95.76 ± 1.85 | 95.84 ± 1.52 | 95.11 ± 1.90 | |
Precision | LDA | 89.31 ± 4.50 | 87.85 ± 4.04 | 86.50 ± 5.79 | 83.86 ± 6.16 | 81.59 ± 8.02 | 82.20 ± 6.19 | 78.79 ± 8.44 |
SVM | 89.54 ± 4.71 | 88.01 ± 4.13 | 86.71 ± 5.96 | 83.62 ± 6.46 | 81.90 ± 7.86 | 82.43 ± 6.31 | 79.36 ± 8.34 | |
KNN | 88.01 ± 5.31 | 86.51 ± 5.04 | 84.92 ± 6.86 | 81.21 ± 7.73 | 79.32 ± 9.12 | 79.56 ± 7.64 | 76.19 ± 9.59 | |
F1 Score | LDA | 88.81 ± 4.58 | 87.16 ± 4.17 | 85.69 ± 5.85 | 83.20 ± 6.16 | 80.86 ± 7.91 | 81.54 ± 6.15 | 77.90 ± 8.31 |
SVM | 89.06 ± 4.81 | 87.36 ± 4.27 | 85.93 ± 6.09 | 83.00 ± 6.44 | 81.23 ± 7.89 | 81.85 ± 6.33 | 78.59 ± 8.35 | |
KNN | 87.56 ± 5.37 | 85.93 ± 5.09 | 84.24 ± 6.89 | 80.69 ± 7.64 | 78.67 ± 9.09 | 79.04 ± 7.58 | 75.44 ± 9.50 |
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Paper | Subject Type | Muscle Force Level | Feature | Classifier | Training Force | Accuracy (%) | Comment |
---|---|---|---|---|---|---|---|
Tkach et al. [24] | Intact | Low and high | Mean absolute value, zero crossings, slope sign change, waveform length, Wilson amplitude, variance, v-order, log detector, EMG histogram, AR, and cepstrum coefficients. | LDA | Low and high | 82 with AR | Time-domain features are not stable with muscle force variation. |
Huang et al. [25] | Intact | --- | Mean absolute value, zero crossings, slope sign change, waveform length, AR, and RMS | Gaussian mixture model | --- | 96 AR + RMS | AR and RMS can be grouped for better EMG pattern recognition performance. |
Scheme et al. [20] | Intact | 20% to 80% of MVC at 10% interval | Time-domain features | LDA | 20% to 80% | 84 | Time-domain features are not reliable with muscle force variation. |
Al-Timemy et al. [19] | Amputee | Low, medium, and high | TDPSD includes root squared zero-order, second-order, and fourth-order moments; sparseness; irregularity factor; and waveform length ratio | LDA | All | 90 | TDPSD improves the performance with muscle force variation. |
Khushaba et al. [26] | Intact and amputee | --- | TSD, which includes root squared zero-order, second-order, and fourth-order moments; sparseness; irregularity factor; coefficient of variation; and Teager–Kaiser energy operator | LDA | --- | 99 (128 channel EMG) | TSD improves the EMG pattern recognition performance |
He et al. [27] | Intact | Low, medium, and high | Global normalized discrete Fourier transform-based features | LDA | Medium | 91 | Force-invariant EMG pattern recognition performance is satisfactory, but the electrode position is specific. |
Khushaba et al. [32] | Intact (driver drowsiness detection) | --- | Symmlet-8 decomposition-based Wavelet features including energy, variance, standard deviation, waveform length, and entropy | LDA | --- | 97 | Performance is better in another field, so the features may be applicable for force-invariant EMG pattern recognition. |
Du et al. [33] | Intact | --- | Time-domain features (TDF) including the integral of EMG, waveform length, variance, zero-crossing, slope sign change, and Wilson amplitude | Grey relational analysis | --- | 96 | Performance is better, so these features may be utilized for force-invariant EMG pattern recognition. |
Hudgin et al. [34] | Intact and amputee | --- | Mean absolute value, mean absolute value slope, zero crossings, slope sign change, and waveform length | Neural Network | --- | 91.2 for intact subject and 85.5 for amputee | Performance is not satisfactory for amputees, but the features are fundamental. |
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Islam, M.J.; Ahmad, S.; Haque, F.; Reaz, M.B.I.; Bhuiyan, M.A.S.; Islam, M.R. Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees. Diagnostics 2021, 11, 843. https://doi.org/10.3390/diagnostics11050843
Islam MJ, Ahmad S, Haque F, Reaz MBI, Bhuiyan MAS, Islam MR. Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees. Diagnostics. 2021; 11(5):843. https://doi.org/10.3390/diagnostics11050843
Chicago/Turabian StyleIslam, Md. Johirul, Shamim Ahmad, Fahmida Haque, Mamun Bin Ibne Reaz, Mohammad Arif Sobhan Bhuiyan, and Md. Rezaul Islam. 2021. "Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees" Diagnostics 11, no. 5: 843. https://doi.org/10.3390/diagnostics11050843