No abstract

Background. Gait alterations are hallmarks for the diagnosis and follow-up of patients with Parkinson’s disease (PD). In normal conditions, age could affect gait dynamics. Although it is known that objective assessment of gait is a valuable tool for diagnosis and follow-up of patients with PD, only few studies evaluate the effect of aging on the gait pattern of patients with PD. Objective. The purpose of this study was to assess differences in gait dynamics between PD patients and healthy subjects and to investigate the effects of aging on these differences using a low-cost RGB-D depth-sensing camera. Methods. 30 PD patients and 30 age-matched controls were recruited. Descriptive analysis was used for clinical variables, and Spearman’s rank correlation was used to correlate age and gait variables. The sample was distributed in age groups; then, Mann–Whitney U test was used for comparison of gait variables between groups. Results. PD patients exhibited prolonged swing (p=0.002) and stance times (p<0.001) and lower speed values (p<0.001) compared to controls. This was consistent in all age groups, except for the one between 76 and 88 years old, in which the controls were slower and had longer swing and stance times. These results were statically significant for the group from 60 to 66 years. Conclusion. Gait speed, swing, and stance times are useful for differentiating PD patients from controls. Quantitative gait parameters measured by an RGB-D camera can complement clinical assessment of PD patients. The analysis of these spatiotemporal variables should consider the age of the subject.

In patients with Parkinson’s disease (PD), arm swing changes are common, even in the early stages, and these changes are usually evaluated subjectively by an expert. In this article, hypothesize that arm swing changes can be detected using a low-cost, cloud-based, wearable, sensor system that incorporates triaxial accelerometers. The aim of this work is to develop a low-cost, assistive diagnostic tool for use in quantifying the arm swing kinematics of patients with PD. Ten patients with PD and 11 age-matched, healthy subjects are included in the study. Four feature extraction techniques were applied: (i) Asymmetry estimation based on root mean square (RMS) differences between arm movements; (ii) posterior–anterior phase and cycle regularity through autocorrelation; (iii) tremor energy, established using Fourier transform analysis; and (iv) signal complexity through the fractal dimension by wavelet analysis. The PD group showed significant (p < 0.05) reductions in arm swing RMS values, higher arm swing asymmetry, higher anterior–posterior phase regularities, greater “high energy frequency” signals, and higher complexity in their XZ plane signals. Therefore, the novel, portable system provides a reliable means to support clinical practice in PD assessment.

No abstract