Gait analysis for Parkinson's disease using multiscale entropy.

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by motor dysfunction and complex gait abnormalities. Traditional linear methods often fail to capture the intricate movement patterns in PD. This review highlights Multiscale Entropy (MSE) as a promising tool for assessing gait dynamics, offering deeper insights into movement variability across multiple temporal scales. MSE distinguishes healthy and pathological gait patterns, enhancing early diagnosis and disease monitoring. Advances in wearable sensors, artificial intelligence, and machine learning have boosted MSE's clinical relevance by enabling real-time, personalized gait assessments. Despite these benefits, MSE faces challenges such as computational demands and the need for high-resolution data. Addressing these limitations through large-scale studies, standardized protocols, and integration of emerging technologies may support broader clinical adoption and the development of a robust normative database.