Table 5.

Characteristics of included artificial intelligence studies of custom algorithms.

Authors	Study objectives	Sensors	Algorithm or model	Variables	Model accuracy	Quality assessment score
Buckley et al. 26	To find out whether changes in upper body motion (accelerations) during gait is a predictor of early PD.	Pressure-sensitive mat, inertial sensors	Univariate regression analysis, multivariate regression analysis	Spatiotemporal characteristics, upper body accelerations	Univariate AUC: 0.70–0.81; Multivariate AUC: 0.88–0.91	8.5
Fino et al. 27	To examine whether horizontal head turns when seated or walking have the clinical utility for diagnosing acute concussion.	Inertial sensors	Linear mixed models (LMMs)	Gait speed, peak head angular velocity, peak head angle, response accuracy, clinical balance	Peak head angular velocity: 0.7 < AUC < 0.8; peak head angle: 0.6 < AUC < 0.7	8.0
Ilg et al. 28	To identify gait features that allow quantification of ataxia-specific gait features in real life (participants with cerebellar ataxia).	Inertial sensors	Kruskal-Wallis test, Mann-Whitney U-test, Friedman test, Wilcoxon signed-rank test	Stride variability, lateral step variability	Lateral step deviation and a compound measure of spatial step variability: 0.86 accuracy	9.0
Mc Ardle et al. 10	To differentiate dementia subtypes (AD, DLB, PDD) using gait analysis.	Inertial sensors, instrumented walkway	One-way analysis of variance (ANOVA), Kruskal-Wallis test	Pace, Variability, Rhythm, Asymmetry, Postural Control	Wearable sensors: 7 out of 14 gait characteristics; instrumented walkway: 2 out of 14 gait characteristics showed significant group differences	9.5
Simila et al. 29	To predict early signs of balance deficits using wearable sensors.	Inertial sensors	Mann-Whitney U-test, fast Fourier transform (FFT) algorithm. Generalised linear models. Sequential forward floating selection (SFSS) method and ten-fold cross-validation	Step time, stride time	AUC 0.78 is predicting decline in total Berg Balance Scale (BBS) and 0.82 for one leg stance.	8.0
Stack et al. 30	To evaluate the usability of wearable sensors in detecting balance impairments in people with parkinson Disease in comparison with traditional methods (observation).	Inertial sensors, video analyst	N/A	Stability, subtle instability (caution and near-falls), time taken, parkin activity scale (PAS)	Ratings agreed in 86/117 cases (74%) for both video analysts and wearable sensors data. (highest for chair transfer, TUG, 3 m walk)	7.5
Tesconi et al. 31	To investigate the possibility of using wearable sensors for monitoring flexion-extension of the knee joint during deambulation.	Knee-band, wearable sensor, and sensorised shoe	N/A	Voltage level (flexion-extension signals), irregularity parameter (gait discontinuity)	Central sensors: sensitivity 80% specificity 75%; lateral sensors: sensitivity 80% specificity 100%	7.0
Zhang et al. 32	To differentiate post-stroke patients from healthy controls using wearable sensors and proposed gait symmetry index GSI.	Inertial sensors	Wilcoxon test; Cliff's delta; Spearman Correlation; Pearson correlation coefficient	Spatiotemporal parameters, foot pitch angular velocity	The proposed GSI of L3 has good discriminative power in differentiating post-stroke patients.	8.5
Zhou et al. 33	To examine whether remotely monitoring mobility performance can help identify digital measures of cognitive impairments in haemodialysis patients.	Inertial sensors	Analysis of variance (ANOVA); Analysis of chi-squared, Analysis of covariance (ANCOVA), univariate and multivariate linear regression model, binary logistic regression analysis	Cumulated posture duration, daily walking performance, postural-transition	Highest AUC 0.93 model include demographics and all variables (accuracy of 85.5%)	8.5