Figure 1:
An overview of the gesture recognition system.
Figure 2:
Evaluation of five hand gestures designed for holding, grasping, and relaxing activities.
Figure 3:
The precision impedance analyzer WK6632.
Figure 4:
The electrode for gesture recognition data acquisition.
Figure 5:
Electrode configuration for impedance data collection: using four electrodes (E1-E2, E1-E3, E1-E4, E2-E3, E2-E4, and E3-E4).
Figure 6:
Comparative SNR performance graph for five hand gestures (A, B, C, D, and E), with each gesture measured for 208 values (04 electrodes) and conducted with 200 repetitions.
Figure 7:
The measurements obtained from electrode pairs across various gestures for three volunteers with 5 gestures A, B, C, D, and E: (a) Person 1, (b) Person 2, (c) Person 3.
Figure 8:
Investigating PCA for gesture recognition with 2 dimensions.
Figure 9:
Investigating PCA for gesture recognition with three dimensions.
Figure 10:
Standard deviation values for sample data from 3 volunteers with labeled gestures A, B, C, D, and E for Person 1, Person 2, and Person 3.
Table 1:
Performance evaluation of machine learning models.
| Model | Fold | Accuracy | Precision | Recall | F1-Score |
|---|---|---|---|---|---|
| KNN | Fold-1 | 0.70 | 0.72 | 0.70 | 0.72 |
| Fold-2 | 0.93 | 0.96 | 0.93 | 0.92 | |
| Fold-3 | 0.91 | 0.92 | 0.91 | 0.90 | |
| Fold-4 | 0.94 | 0.96 | 0.94 | 0.94 | |
| Fold-5 | 0.81 | 0.87 | 0.81 | 0.79 | |
| Mean | 0.86 | 0.89 | 0.86 | 0.85 | |
| GBM | Fold-1 | 0.94 | 0.96 | 0.94 | 0.94 |
| Fold-2 | 0.93 | 0.94 | 0.93 | 0.92 | |
| Fold-3 | 0.76 | 0.63 | 0.76 | 0.68 | |
| Fold-4 | 0.95 | 0.95 | 0.95 | 0.95 | |
| Fold-5 | 0.70 | 0.79 | 0.70 | 0.69 | |
| Mean | 0.86 | 0.86 | 0.86 | 0.84 | |
| Naive Bayes | Fold-1 | 0.72 | 0.73 | 0.72 | 0.71 |
| Fold-2 | 0.93 | 0.96 | 0.93 | 0.92 | |
| Fold-3 | 0.96 | 0.97 | 0.96 | 0.96 | |
| Fold-4 | 0.91 | 0.95 | 0.91 | 0.89 | |
| Fold-5 | 0.70 | 0.74 | 0.70 | 0.67 | |
| Mean | 0.84 | 0.87 | 0.84 | 0.83 | |
| LR | Fold-1 | 0.80 | 0.79 | 0.80 | 0.78 |
| Fold-2 | 0.94 | 0.96 | 0.94 | 0.94 | |
| Fold-3 | 0.93 | 0.94 | 0.93 | 0.92 | |
| Fold-4 | 0.93 | 0.95 | 0.93 | 0.92 | |
| Fold-5 | 0.85 | 0.89 | 0.85 | 0.83 | |
| Mean | 0.89 | 0.90 | 0.89 | 0.88 | |
| Random Forest | Fold-1 | 0.74 | 0.74 | 0.74 | 0.73 |
| Fold-2 | 0.93 | 0.96 | 0.93 | 0.92 | |
| Fold-3 | 0.96 | 0.97 | 0.96 | 0.96 | |
| Fold-4 | 0.93 | 0.96 | 0.93 | 0.92 | |
| Fold-5 | 0.81 | 0.89 | 0.81 | 0.79 | |
| Mean | 0.87 | 0.90 | 0.87 | 0.86 | |
| SVM | Fold-1 | 0.72 | 0.73 | 0.72 | 0.71 |
| Fold-2 | 0.94 | 0.96 | 0.94 | 0.94 | |
| Fold-3 | 0.94 | 0.95 | 0.94 | 0.94 | |
| Fold-4 | 0.94 | 0.96 | 0.94 | 0.94 | |
| Fold-5 | 0.80 | 0.86 | 0.80 | 0.78 | |
| Mean | 0.87 | 0.89 | 0.87 | 0.86 |
Figure 11:
Confusion matrices demonstrating the classification accuracy of machine learning models for hand gesture recognition.