Table 1 A summary of hybrid neural-fuzzy techniques applied to EMG analysis
From: Hybrid soft computing systems for electromyographic signals analysis: a review
Reference | Task | Techniques | Results |
|---|---|---|---|
[35] | Classification: 10 hand motions | Fuzzy-C means + MLP | Accuracy: 99% |
[35] | Classification: hand motions | Fuzzy entropy + MLP | Accuracy: 99% |
[36] | Classification: 2 leg motions | Surgeno model + RBF | Accuracy: 57/60 |
[37] | Classification: 7 arm motions | Surgeno model + MLP | Accuracy: 86% |
[38] | Classification: 6 hand motions | Fuzzy clustering NN | Accuracy: 95% ~ 100% |
Classification: 6 classes hand motions | ANFIS | Accuracy: 96.7±1.2% [40] Accuracy: maximum 100%, mean 92% [41] Accuracy: mean 94% [42] | |
[42] | Prediction gait events | ANFIS | Accuracy: 95.3%. ~ 98.6%. |
Classification: 3 arm motions | Abe-Lan fuzzy network | Abe-Lan fuzzy network performed better than SOM, FCM, and MLP | |
[45] | Classification: 6 motions | Fuzzy Min-Max ANN | Accuracy: 10% higher than without fatigue compensation |
[46] | Classification: 4 hand motions | Fuzzy SVM | Accuracy: Fuzzy SVM outperformed BP NN 5% |
[47] | Classification: 6 hand motions | FuzzyEn + ELM, CrEn + ELM | CrEn outperformed FuzzyEn |
[48] | Classification: 10 grasps or in-hand manipulations | FGMM | Accuracy: 96.7% of FGMM, better than HMM, SVM |
Modelling: EMG-movements | ANFIS | Accuracy: 97%, 99%, 87.9%, and 81.8% for four subjects, respectively | |
[51] | Modelling: force moment and velocity-peak EMG | Neuro-fuzzy | Error: 4.97% ~ 13.16% |
Modelling: kinematics-EMG-force | RFNN | Small prediction error | |
[54] | Modelling: EMG-force moment | Takagi-Sugeno | EMG-to-activation model performed better than Takagi-Sugeno |
Control upper-limb exoskeleton | Neuro-fuzzy | Effectiveness of the control method | |
[7] | Control upper-limb exoskeleton | Neuro-fuzzy | Low RMS errors |
[57] | Control ankle exoskeleton | Neuro-fuzzy | Low RMS errors |
[58] | Diagnosis | Fuzzy integral + BP NN | Accuracy: 80.95±7.2% |
[16] | Diagnosis | FSVM | Accuracy: 93.5±1.4% FSVM performed better than LDA, BP and RBF |
[59] | Decomposition | AFNNC | Accuracy: AFNNC performed better than ACC at roughly 6.1% |
[60] | Diagnosis | NEFCLASS | Accuracy: 90% |
[61] | Diagnosis | ANFIS | Accuracy: 76.43% |