Research Article
A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements
Table 4
Comparison of the number of movements and classification accuracy of the proposed method with existing algorithms.
| Author | Classification algorithm | Sensing material | Number of movements | Accuracy of classification (%) |
| Nassour et al. [1] | A linear regression | Electrolyte solution resistance | 15 | 89.4 | Chen et al. [2] | Support vector machine | Flexion sensors and force sensors | 16 | 89.4 | Pan et al. [3] | Extremely randomized trees | Capacitive pressure sensors | 10 | 99.7 | Maitre et al. [4] | Random forest | The force and bending sensors | 8 | 95 | Lee and Bae [5] | LSTM | Eutectic gallium indium sensors | 11 | 100 | Ayodele et al. [6] | CNN | Weft knit sensors | 6 | 88.27 | Chauhan et al. [7] | Gaussian process | Rotary potentiometers | 5 | 75 | Fatimah et al. [8] | Ensemble subspace discriminant | Surface electromyogram | 17 | 93.53 | Maitre et al. [9] | CNN-LSTM | The force and bending sensors | 13 | 93 | Huang et al. [10] | Dynamic time warping | RGO-coated fiber sensors | 9 | 98.3 | Lun et al. [11] | GCNs-Net | Electroencephalogram | 4 | 96.24 | Hou et al. [12] | BiLSTM-GCN | Electroencephalogram | 4 | 98.81 | Proposed work | DBDF | Flexion sensors (CyberGlove II) | 52 | 93.15 |
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