Using Gait to Monitor Health: An Experimental Baseline

Jorge Zafra-Palma, Nuria Marín-Jiménez, José Castro-Piñero, Magdalena Cuenca-García, Rafael Muñoz-Salinas, Manuel J. Marín-Jiménez

Abstract: In this work, we explore the use of gait as a key indicator of health and anthropomorphic measurements. We support the idea that with the progress of Computer Vision techniques, the visual information associated with an individual’s walking pattern may provide valuable health-related insights in a non-invasive manner, i.e., without the need for attached or task-specific expensive sensors. The main contribution of this work is an experimental baseline that shows promising results in the inference of demographic and anthropometric characteristics based on visual information and gait parameters obtained from the recent Health & Gait dataset. We employ various data representations, including silhouette, semantic segmentation, and optical flow, as well as gait parameters measured by sensor-based systems and estimated from video-based pose information. Our findings highlight the potential of gait analysis for non-intrusive and accessible health monitoring and the estimation of demographic and anthropometric characteristics that may indicate underlying health conditions. This approach offers a scalable and privacy-preserving alternative to traditional diagnostic methods, contributing to the development of preventive healthcare strategies.

External IDs:dblp:conf/ibpria/ZafraPalmaMCCMM25