Motion Data in Neurology

Computational intelligence and digital signal processing are essential mathematical tools widely applied in biomedical and engineering domains. Gait symmetry analysis is particularly important for detecting motion disorders in neurology, rehabilitation, and sports science. This study presents a methodology for motion analysis using time-synchronized accelerometric and gyrometric sensors to capture dynamic gait patterns. Data were collected from 14 healthy controls and 17 individuals with Parkinson's disease-related gait impairments. The proposed approach integrates spectral analysis and digital filtering to remove noise and irrelevant frequency components during preprocessing. Motion classification is performed by analyzing energy distribution using discrete Fourier and wavelet transforms, enabling multilevel signal decomposition. Gait recognition\u2014distinguishing between normal and abnormal patterns\u2014is based on energy components in selected frequency bands and their ratios. Neural network classifiers achieved the highest performance, with an accuracy of 96.8% and a cross-validation error of 0.097, using data from sensors placed on the left and right sides of the body. Motion asymmetry detected by the model aligned with clinical assessments in 88% of cases, based on a threshold set at 1.1 times the mean value of symmetric coefficients of the normal gait. These findings highlight the potential of artificial intelligence and signal processing in supporting the clinical diagnosis of neurological disorders such as Parkinson's disease.