Table 6.
Comparison of the proposed methods with methods developed in related studies.
| Author | Year | Methods | Task | Sample Size | Performance |
|---|---|---|---|---|---|
| Di Lazzaro G. et al. [48] | 2020 | SVM | motor | 65 | ACC: 97% (SVM) |
| Yuhan Zhou et al. [50] | 2020 | SVM | gait | 239 | ACC: 89% (SVM) |
| RF | ACC: 73% (RF) | ||||
| ANN | ACC: 90% (ANN) | ||||
| Tian Bao et al. [53] | 2019 | SVM | balance | 16 | ACC: 82% (SVM) |
| Jianwei Niu et al. [56] | 2019 | SVM | gait | 12 | ACC: 96.7% (SVM) |
| Narintip Roongbenjawan et al. [59] | 2020 | Cohort Study | balance | 73 | SEN: 92% |
| SPE: 81% | |||||
| The Presented Methods | 2021 | DL + ML | balance | 55 | ACC: 98% (VGG16 + SVM) |
| ACC: 99% (VGG19 + SVM) |
Note: ACC is accuracy. SPE is specificity. RF is random forest. SVM is support vector machine. DL is deep learning. ML is machine learning.