Table 6.

Characteristics of included artificial intelligence studies of machine learning models.

Authors	Study objectives	Sensors	Algorithm or model	Classification accuracy model validation	Classification accuracy results	Quality assessment score (total score 12)
De Vos et al. 34	To investigate the use of wearable sensors coupled with machine learning as a means of disease classification between progressive supranuclear palsy and Parkinson’s disease.	Inertial sensors	One-way-analysis of variance (ANOVA), independent t-test, least absolute shrinkage and selection operator LASSO, LR, RF	10-fold cross validation	93% (RF: PSP vs. HC), 88% (RF: PSP vs. PD), 93% (LR: PSP vs. HC), 80% (LR: PSP vs. PD)	10.0
Di Lazzaro et al. 35	To assess motor performances of a population of newly diagnosed, drug-free Parkinson’s disease (PD) patients using wearable inertial sensors and to compare them to healthy controls and differentiate PD subtypes (tremor dominant, postural instability gait disability and mixed phenotype).	Inertial sensors	ReliefF ranking, Kruskal-Wallis feature-selection methods, SVM, one-way ANOVA	Leave-one-out cross-validation	97% (0.96)	7.5
Hsu et al. 36	To conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analysing and classifying the gaits of patients with neurological disorders.	Inertial sensors	RF, neural network with multilayer perceptron, Gaussian Naïve Bayes, Adaboost, decision tree	5-fold cross validation	81% (RF), 78% (MP), 87% (NB), 84% (AB), 80% (DT)	9.0
Jung et al. 37	To develop and evaluate neural network-based classifiers for effective categorization of athlete, normal foot, and deformed foot groups’ gait assessed using wearable IMUs.	Inertial sensors	Neural network-based classifier (using gait spectrograms) CNN	4-fold cross-validation	Neural network Gait parameters: 93.02%. Gait spectrograms of foot (88.82%); post pelvis (94.25%); foot and post pelvis (96.68%); foot, shank, thigh, post pelvis (98.19%)	8.0
Kashyap et al. 38	The study examines the potential use of a comprehensive sensor-based approach for objective evaluation of cerebellar ataxia in five domains (speech, upper limb, lower limb, gait and balance) through the instrumented versions of nine bedside neurological tests.	Inertial sensors, Kinect depth camera	RF, principal component analysis (PCA)	Leave-one-out cross-validation, comparing mean squared errors MSE by classification of various leaf size (l)	Combined nine tests demonstrate performance accuracy: 91.17%; classifying principal components PC in decreasing order of importance, performance accuracy: 97.06%; RF 97% (F1 score = 95.2%)	7.5
Mannini et al. 39	To propose and validate a general probabilistic modelling approach for the classification of different pathological gaits (healthy elderly, post-stroke patients, patients with Huntington disease).	Inertial sensors	Hidden Markov models (HMMs), SVM, majority voting (MV) classification	Leave-one-subject-out (LOSO) cross-validation	Overall accuracy: 66.7%; SVM classifier (HMM-based features only): 71.5%; SVM classifier (time and frequency domains only): 71.7%; SVM classifier (full feature): 73.3%; apply MV classifier with the full feature: 90.5%	9.5
Moon et al. 40	To determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilised to differentiate between Parkinson’s disease (PD) and essential tremor (ET) using machine learning.	Inertial sensors	Neural networks, SVM, k-nearest neighbour, decision tree, RF, gradient boosting, LR, dummy model	Accuracy, recall, precision, F1 score, 3 fold cross-validation with grid search strategy	Accuracy 0.65 (kNN) to 0.89 (NN); precision 0.54 (SVM, kNN, DT, LR) to 0.61(NN); recall 0.58 (DT) to 0.63 (kNN, GB), F1 score 0.53 (DT, LR) to 0.61 (NN)	8.0
Nukala et al. 41	To accurately classify patients with balance disorders and normal controls using wearable inertial sensors using machine learning and to determine the best-performing classification algorithm.	Inertial sensors	Back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbours algorithm (KNN), binary decision tress (BDT)	Confusion Matrix, sensitivity, specificity, precision, negative predictive value (NPV), F-measure	BP-ANN (100%), SVM (98%), KNN (96%), BDT (94%)	7.0
Rastegari 42	To compare the bag-of-words approach with standard epoch-based statistical approach of feature engineering methods in Parkinson disease patients.	Inertial sensors	Linear SVM, decision tree, RF, k-nearest neighbour KNN	Leave-one-subject-out cross validation LOSOCV, Pearson correlation analysis	Bag of words approach (90%), epoch-based statistical feature (60%)	7.0
Rehman et al. 43	To compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of Parkinson’s disease.	Inertial sensors	Multivariate analysis of variance (MANOVA), independent t-test, receiver operating characteristics analysis (ROC), SVM with radial basis function SVM-RBF, RF	10-fold cross-validation with area under the curve AUC	SVM performed better than RF. Intermittent walkway (IW): no differences between Axivity and GAITRite; continuous walkway (CW): classification more accurate with Axivity (AUC 87.83) versus GAITRite (AUC 80.49)	9.0
Rovini et al. 44	To accurately classify gait altering pathologies (healthy controls, Parkinson’s disease, idiopathic hyposmia) by performing comparative classification analysis using three supervised machine learning algorithms.	Inertial sensors	Kolmogorov-Smirnov test, Kruskal-Wallis test, Wilcoxon rank-sum test, Spearman correlation coefficient, SVM, RF, Naïve Bayes NB	Sensitivity, specificity, precision, accuracy, F-measure, 10-fold cross-validation	RF (0.97 accuracy); SVM (0.93 sensitivity, 0.97 specificity, 0.95 accuracy); NB (0.95 accuracy). SVM had the worst performance.	6.5
Steinmetzer et al. 45	To determine whether arm swing could accurately classify patients with Parkinson’s disease using wearable sensors and machine learning algorithms.	Inertial sensors	Wavelet transformation, convolutional neural network CNN single signal, multi-layer CNN, weight voting	3-fold cross-validation, binary confusion matrix	Three-channel CNN achieved the best results. classification accuracy of 93.4% using wavelet transformation and three-layer CNN architecture	9.0
Tedesco et al. 46	To investigate the ability of a set of inertial sensors worn on the lower limbs by rugby players involved a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning.	Inertial sensors	k-nearest neighbour kNN, Naïve Bayes NB, SVM, gradient boosting tree XGB, multi-layer perceptron MLP, stacking	Leave-one-out cross-validation LOSO-CV	Multilayer perception accuracy 73.07%; gradient boosting sensitivity 81.8%. Worst accuracy SVM 71.18%. The overall accuracy is uniform among all models (between 71.18% and 73.07%)	9.5

Abbreviations : LR, Logistic Regression; RF, Random Forest; SVM Support Vector Machine; ANOVA, One-way-Analysis Of Variance.