Background: Arm swing changes are common even in the early stages of Parkinson’s disease (PD). We hypothesized that arm swing changes decrease with age and can be detected using a low-cost, RGB-D depth-sensing camera.Objective: This study aimed to assess the differences in arm swing between PD patients and healthy participants and to investigate the possible effects of aging on these differences.Methods: Twenty-five PD patients (aged 45–87 years) and 25 age-matched, healthy subjects (aged 46–88 years) were included. Clinical variables were evaluated using a descriptive analysis. No spatiotemporal variables were normally distributed; therefore, we used a Mann–Whitney U test to compare the continuous variables between groups and to perform age-stratified analysis. A receiver operating characteristic analysis was generated to evaluate the discrimination activity of arm swing asymmetry (ASA).Results: The PD group showed significant reductions in arm swing magnitude (left, p = 0.002; right, p = 0.006) and arm swing speed (left, p = 0.002; right, p = 0.004) and significantly greater ASA (p < 0.001). The age-stratified analysis showed significant differences in ASA in the 40–59-year group (p = 0.001) and bilateral arm swing magnitude in the 60–66-year group. No differences were found in those aged >67 years.Conclusions: The camera detected differences in ASA, arm swing speed, and arm swing magnitude between PD patients and healthy individuals. Analysis of arm swing variables should be stratified by age, and the validity of the analysis may be questionable in patients aged >67 years.

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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.

Parkinson's disease is characterized by alterations in the gait pattern that may increase the risk of falls. Variations in the gait pattern cannot be objectively measured in clinical examination, so it is necessary to adapt devices to measure objectively, valid and replicable changes in gait patterns that are part of the evolution of the disease and / or pharmacotherapy. In an interdisciplinary effort, we developed the "e-Motion Capture System" software, which is able to calculate motor (cadence, stride and step length) and spatiotemporal (velocity and acceleration) parameters that affect quality of life in patients with Parkinson's disease. In this paper, we show results of the comparison between our e-Motion software and a benchmark reference, multiple-camera 3D motion capture system to track a gait pattern. This analysis was performed to compare the spatial locations of the ankles of a volunteer under indoor controlled conditions. Our results for the comparison between e-Motion and the 3D motion capture system show excellent agreement.

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