Avaliação de desenhos senoidais de indivíduos com a doença de Parkinson utilizando acelerometria

Abstract

Tremor, stiffness, and bradykinesia are all symptoms of Parkinson's disease (PD). These symptoms impair motor skills, which are primarily performed by the hands and fingers. Currently, one of the most common measures used by health professionals is the use of clinical scales that rely on subjective analysis. This study aimed to extract information from inertial sensors in order to quantitatively evaluate the fine motor performance of people with Parkinson's disease. The study included two groups: one of healthy people (CG) and one of people with Parkinson's disease (PG) who had visible tremors in their hands as a result of the disease. Inertial sensors were placed on the back of each volunteer's hand. These signals were collected while participants were drawing a sinusoidal pattern with and without the aid of a printed outline on a piece of paper. The normalized area under the curve (AuC) and the difference between peaks and valleys of the reference correlogram in relation to the estimated correlogram of the collected signals were estimated from the correlogram obtained by applying the autocorrelation function (ACF) on the collected signals (vmm). These characteristics allowed discrimination between the design patterns of CG and PG. For the activity of contouring the sinusoid, the obtained result was (vmm = 0.156 ± 0.059, AuC = 0.211 ± 0.032) for GC and (vmm = 0.246 ± 0.085, AuC = 0.181 ± 0.036) for PG. After normality verification (Shapiro-Wilk, p > 0.05), the t-test was applied to confirm significant differences between the groups (p < 0.05). Cohen's d-effect size (p < 0.05) was large (0.903) for AuC and vmm (1.238). The results for the task of drawing the sinusoid freely for GC were (vmm = 0.261 ± 0.089, AuC = 0.186 ± 0.031) and for PG (vmm = 0.333 ± 0.086, AuC = 0.135 ± 0.041). The Wilcoxon-Mann-Whitney test was applied to vmm and the t test to AuC. Both tests confirmed significant differences between groups (p < 0.05). The size of the effect was large for the two characteristics: δ cliff (0.486) and d-Cohen (1.366). This allowed researchers to quantify the volunteers' motor performance and confirm that people with Parkinson's disease had poorer motor performance when completing the tasks. The evaluation of sine patterns using a correlogram estimated from inertial signals proved to be a simple and practical method for quantifying the motor performance of people with Parkinson's disease in this study.