Evaluation of gait disorders using accelerometric and gyroscopic data for assessment of neurological diseases

Abstract

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 signal preprocessing. Motion classification is performed by analyzing energy distribution using discrete Fourier and wavelet transforms, enabling multilevel signal decomposition. Gait recognition—distinguishing between normal and abnormal patterns—is based on energy components in selected frequency bands and their ratios. Neural network classifiers achieved the highest performance, with a mean accuracy of 81.1% and a cross-validation error of 0.123, using data from sensors placed on the left and right sides of the body. Motion asymmetry detected by the model agreed with assessments of neurologists in 88% of cases. Results of this validation highlight the potential of frequency and scale domain analysis, digital signal processing, and artificial intelligence use in supporting the clinical diagnosis of Parkinson’s disease and further neurological disorders.