Table 2 A summary of hybrid neural-evolutionary techniques applied to EMG analysis
From: Hybrid soft computing systems for electromyographic signals analysis: a review
Reference | Task | Techniques | Results |
|---|---|---|---|
[71] | Classification: 6 arm motions | GA + MLP + HMMs | Accuracy: 87.7% |
Classification: 7 wrist motions | GA + BP ANN | Accuracy: 5% ~ 10% [74, 75], 10% [76] improvement compared to without GA optimization | |
[74] | Classification: 7 wrist motions | GA + MLP | Feature reduction rate: 70% Accuracy: 6% improvement |
[75] | Classification: 6 hand motions | GA + MLP | Error: 4.89% |
[76] | Classification: 4 hand motions | GA + BP ANN | Accuracy: 91.38% |
[77] | Classification: 4 hand motions | GA + RBF + MLP | Accuracy: 6% improvement compared to MLP |
[78] | Classification: 4 hand motions | GA + FFT + PCA + MLP | Improved accuracy and speed |
[79] | Modelling: EMG-on/off signals during stride | GA + BP ANN | Improved speed |
[80] | Classification: 12 finger motions | GA + SVM | Reducing 8 ~ 11 channels and comparable accuracy |
[81] | Classification: 10 hand motions | GA + BP ANN | Accuracy: 98% |
[82] | Classification: 6 wrist motions | GA + RBF | Accuracy: 75% |
[83] | Classification: 2 muscle states | GA + SVM | Accuracy: 97.3% |
[84] | Modelling: EMG-force | GA + BP ANN | Accuracy: 99% |
[85] | Diagnosis | GBLS | Accuracy: 95% for training, 70% for test |