Scientific Data Visualization Tool for Enhanced Gait Pattern Recognition Using Sensorized Insoles

Data visualization is a multidisciplinary domain that applies cognitive and perceptual principles to transform complex datasets into clear and understandable visual representations that guide decision-making. This work introduces a specialized data-visualization tool designed to support professionals and researchers in human gait and its underlying patterns. To collect data, non-invasive sensorized insoles are used, which provide valuable insights into the user’s movement without disrupting their natural gait. These insoles capture precise biomechanical data, including pressure distribution and movement dynamics, which are then analyzed through the tool interface. Such research contributes to the early detection of neurodegenerative disorders, where timely and accurate analysis of gait characteristics can enhance diagnostic precision and improve patient outcomes. The tool offers an intuitive interface and domain-specific analytical workflows that make it accessible to professionals and researchers without requiring extensive training in data analysis. Moreover, the integration of post-processed data visualization, including heatmaps and center of pressure animations, facilitates the identification of subtle gait abnormalities, which may otherwise go unnoticed in traditional clinical settings. By accelerating the progress of gait analysis research, the proposed tool has the potential to facilitate clinical advancements, ultimately leading to better monitoring, diagnosis, and treatment of neurodegenerative diseases.