Editorial: Advancements in Movement Analysis for Understanding Neurological Disorders - Carozzo Simone; Riccardo Spanò (International Journal of Brain Sciences, 2024)
Neurological disorders pose a significant challenge, demanding innovative approaches for accurate diagnosis, effective treatment, and deeper understanding. Movement analysis emerges as a powerful tool, offering a quantitative window into the complexities of motor function. This editorial delves into the transformative impact of movement analysis on neurological research and clinical practice. Traditional diagnostic methods, reliant on subjective observations, often miss subtle motor impairments, particularly in early disease stages. Movement analysis tackles this challenge by capturing nuanced abnormalities in gait, balance, and coordination, enabling early detection and intervention. Furthermore, it provides a standardized framework for monitoring disease progression, treatment efficacy, and potential side effects, crucial for effective disease management. Beyond diagnosis and monitoring, movement analysis offers insights into the underlying pathophysiology of neurological disorders. By correlating characteristic movement patterns with neural correlates, researchers gain valuable knowledge about disease etiology, paving the way for targeted therapies. The convergence of wearable sensor technology, motion tracking systems, and machine learning techniques has transformed the field of movement analysis. This transformation has made it more widely available, economical, and insightful. Such progress offers significant potential for enhancing our knowledge of neurological conditions and bettering the health results of patients.. In conclusion, movement analysis stands as a beacon of hope, offering unparalleled insights into the nervous system. By embracing this quantitative paradigm, we can enhance diagnostic accuracy, objectively monitor disease progression, unveil underlying pathophysiology, and bridge the gap between research and clinical practice.
Keywords: Gait analysis; Machine Learning; Movement analysis; Neurological disorders; Rehabilitation; diagnosis