Wristbands Containing Accelerometers for Objective Arm Swing Analysis in Patients with Parkinson's Disease.

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.

Spatiotemporal Gait Variables Using Wavelets for an Objective Analysis of Parkinson Disease.

Parkinson's disease generates a special interest in factors such as gait patterns, posture patterns, and risk of falls. The human gait pattern has a basic unit called the gait cycle, composed of two phases: stance and swing. Using gait analysis it is possible to get spatiotemporal variables as walking speed and step number derived from stance and swing phases. In this paper, we explore the feasibility of wavelet techniques to analyze gait signals, we use a member of Daubechies family to distinguish automatically gait phases, this approach allowed us to estimate spatiotemporal variables that shows significant differences between Parkinson patients and non-Parkinson patients, this result aims to allow clinical experts to easily diagnose and assess Parkinson patients, with short evaluation times and with non-invasive technologies.