Table 1 Related studies on facial gesture recognition
From: EMG-based facial gesture recognition through versatile elliptic basis function neural network
Reference | Classes | Channels | Feature(s) | Classifier(s) | Result(s) | Application |
|---|---|---|---|---|---|---|
[6] | 5 | 3 | MAV | SVM | 89.75-100% | Control a virtual robotic wheelchair |
[7] | 5 | 3 | RMS | SFCM | 93.2% | Control a virtual interactive tower crane |
[8] | 6 | 8 | AV | GM | 92% | Recognition system |
[10] | 6 | 2 | - | Thresholding | - | Electric Wheelchair Control System |
[16] | 8 | 3 | RMS | SVM, FCM | 80.4%, 91.8% | Recognition system |
[20] | 3 | 3 | Mean, SD, RMS, PSD | Minimum distance | 94.44% | Recognition system |
[21] | 4 | - | MAD,SD, VAR | KNN, SVM, MLP | 61%, 60.7%, 56.19% | Man–machine interface |
[22] | 5 | 2 | RMS | FCM | 90.8% | Recognition system |
[23] | 10 | 3 | RMS | FCM | 90.41% | Multipurpose recognition system for HMI |
[24] | 8 | 3 | RMS | ANFIS+SFCM | 93.04% | Recognition system for HMI |