Table 1.
Summary of biomechanical gait analysis studies using data science methods and their research question of interest
| Reference | Number of subjects | Initial input features | Dimensionality reduction | Leaning algorithms | Research question of interest |
|---|---|---|---|---|---|
| [13] | 96 | 900 features (9 running kinematic waveforms) | MR (the best 3 angles/waveform) | – | Differences between male and female runners experiencing ITBS at the time of testing and healthy gender- and age-matched runners |
| [2] | 483 | 72 features (running kinematic variables) | MR (the best 8–62 PCs) | SVM (78.4–100%) | Gender- and age-related differences in healthy runners |
| [27] | 34 | 31 features (running kinematic variables) | SFS (the best 6 features) | SVM (100%) | Age-differences in healthy runners |
| [40] | 92 | 51 features (running kinematic and kinetic variables) | Several feature extraction methods → AdaBoost (as part of the classifier) | AdaBoost (84.7–100%) | Differences between gender, shod/barefoot running, and runners with and without PFP |
| [47] | 40 | 505 features (5 running kinematic waveforms) | PCA (the first 3 PCs/waveform) | – | Differences between female runners with previous ITBS and female healthy runners |
| [19] | 72 | 902 features (9 running kinematic waveforms + 2 clinical variables) | PCA (only kinematic waveforms) → SFS (the best 2 PCs) | LDA (78.1%) | Prediction of the response to exercise treatment for patients with PFP |
| [20] | 98 | 604 features (6 walking kinematic waveforms + 4 clinical variables) | PCA (only kinematic waveforms) → SFS (the best 6 PCs and 1 clinical variable) | LDA (85.4%) | Prediction of the response to exercise treatment for patients with knee OA |
| [45] | 200 | 4 features (running kinematic variables) or 100 features (a running kinematic waveform) | PCA, Kernel PCA (the first 7 or 10 PCs) | – | Gender- and age-differences in healthy runners |
| [28] | 11 | 3939 features (39 running marker position waveforms) | PCA, PCA with SVM, ICA → MR | – | Differences between movements resulting from wearing shoes with different midsoles |
| [14] | 121 | 900 features (9 running kinematic waveforms) | PCA (the first 4 PCs/waveform) | HCA | Defining distinct groups of healthy runners and to investigate the practical implications of clustering healthy subjects |
| [67] | 88 | 3636 features (36 running marker position waveforms) | SOFM | k-Means | Defining functional groups of runners and to understand whether the defined groups required group-specific footwear features |