RESUMO
Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.
RESUMO
Asymmetry is a cardinal symptom of gait post-stroke that is targeted during rehabilitation. Technological developments have allowed accelerometers to be a feasible tool to provide digital gait variables. Many acceleration-derived variables are proposed to measure gait asymmetry. Despite a need for accurate calculation, no consensus exists for what is the most valid and reliable variable. Using an instrumented walkway (GaitRite) as the reference standard, this study compared the validity and reliability of multiple acceleration-derived asymmetry variables. Twenty-five post-stroke participants performed repeated walks over GaitRite whilst wearing a tri-axial accelerometer (Axivity AX3) on their lower back, on two occasions, one week apart. Harmonic ratio, autocorrelation, gait symmetry index, phase plots, acceleration, and jerk root mean square were calculated from the acceleration signals. Test-retest reliability was calculated, and concurrent validity was estimated by comparison with GaitRite. The strongest concurrent validity was obtained from step regularity from the vertical signal, which also recorded excellent test-retest reliability (Spearman's rank correlation coefficients (rho) = 0.87 and Intraclass correlation coefficient (ICC21) = 0.98, respectively). Future research should test the responsiveness of this and other step asymmetry variables to quantify change during recovery and the effect of rehabilitative interventions for consideration as digital biomarkers to quantify gait asymmetry.
RESUMO
Parkinson's Disease (PD) can lead to impaired/slowed movement, gait impairments and increased risk of falling. Wearable technology-based gait analysis is emerging as a powerful tool to detect early disease and monitor progression. Here we present a novel approach to producing an objective, compact and personalised overview of a patient's gait pattern. Phase plots were constructed in 41 people with PD and 38 controls (CL) from accelerometry data collected during straight intermittent walks with a single triaxial accelerometer placed on the lower back. Phase plots were analysed using bivariate Gaussian mixture models and classified based on several apparent features derived from the parameters of said model. Significant differences in phase plot form were found between and PD and CL subjects; with a very high within-subject consistency (reproducibility) (p <; 0.0001). PD and CL subjects differ in the types of phase plots produced (p <; 0.001). Strong connections between spatio-temporal (ST) gait characteristics and phase plot types were found. The presented novel methodology not only showed to be sensitive to pathology (PD vs CL), but can quickly produce a unique fingerprint of a person's gait. This work presents encouraging results for clinical application of an objective, personalised gait feature for disease monitoring and clinical applications.
