Table 6.
An overview of the AI techniques applicable for gait analysis.
| References | Input parameters | Technique |
|---|---|---|
| Lau et al. (163) | Kinematic data | Support vector machines (SVM), Artificial neural network (ANN), Radial Basis Function network (RBF), and Bayesian Belief Network (BBN). |
| Lai et al. (56) | Spatiotemporal, kinematic, kinetic, and EMG data | Signal processing and computational intelligence methods. |
| Lau et al. (44) | Kinematics data | SVM, ANN, RBF. |
| Kaptein et al. (164) | kinematic and physiological data | Analysis of variance (ANOVA) supplemented by logistic and partial least squares (PLS) regressions. |
| Laroche et al. (165) | Kinematic trajectories | SVM. |
| Karg et al. (166) | time series gait data | Hidden Markov Model (HMM). |
| Cippitelli et al. (167) | body joint trajectories | Algorithm based on anthropometric models. |
| Joyseeree et al. (168) | Spatiotemporal data | Random Forest (RF), boosting, Multilayer Perceptron (MLP), and SVM. |
| LeMoyne et al. (169) | Temporal and kinetic data | SVM. |
| Ferber et al. (170) | n/a | n/a. |
| Osis et al. (171) | Kinematic data and ground reaction forces | Principal Component Analysis (PCA). |
| Zeng et al. (172) | Vertical GRF | RBF networks. |
| Hannink et al. (173) | Spatiotemporal data | Deep convolutional neural networks. |
| Caldas et al. (103) | IMU data | artificial intelligence (AI) algorithms [e.g., artificial neural networks (ANN) and hidden Markov models (HMM)]. |
| Park et al. (174) | Spatiotemporal and plantar pressure | Random forest classification. |
| Pham and Yan (175) | Vertical GRF | Tensor decomposition. |
| Ertelt et al. (176) | GRF | Bayesian regulated neural networks. |
| Haji Ghassemi et al. (177) | Inertial data | Peak detection, two variants of dynamic time warping (DTW) methods [Euclidean DTW (eDTW) and probabilistic DTW (pDTW)], and hierarchical hidden Markov models (hHMM). |
| Zhan et al. (178) | Stride length | A rank-based machine-learning algorithm called disease severity score learning (DSSL). |
| Zhang et al. (179) | GRF | SVM. |
| Bastien et al. (180) | Ground reaction forces (GRF) | A predictive linear model of the fore-aft GRF. |
| Galbusera et al. (181) | review article | Machine learning and deep learning. |
| Jiang et al. (182) | Inertial data, GRF | Random forest learning. |
| Nguyen et al. (183) | Inertial data | PCA, SVM, ANN. |
| Prado et al. (184) | Temporal data | Recurrent Neural Network classifier model. |
| Waugh et al. (185) | Accelerometer data | Canonical dynamical system (CDS)Fourier series. |
| Jauhiainen et al. (186) | Kinematic data | Cluster analysis. |