RT journal article
T1 Health & Gait: a dataset for gait-based analysis
A1 Zafra Palma, Jorge
A1 Marín Jiménez, Nuria
A1 Castro Piñero, José
A1 Cuenca García, María Magdalena
A1 Muñoz Salinas, Rafael
A1 Marín Jiménez, Manuel J.
A2 Didáctica de la Educación FísicaPlástica y Musical
AB Acquiring gait metrics and anthropometric data is crucial for evaluating an individual’s physical status. Automating this assessment process alleviates the burden on healthcare professionals and accelerates patient monitoring. Current automation techniques depend on specific, expensive systems such as OptoGait or MuscleLAB, which necessitate training and physical space. A more accessible alternative could be artificial vision systems that are operable via mobile devices. This article introduces Health&Gait, the first dataset for video-based gait analysis, comprising 398 participants and 1, 564 videos. The dataset provides information such as the participant’s silhouette, semantic segmentation, optical flow, and human pose. Furthermore, each participant’s data includes their sex, anthropometric measurements like height and weight, and gait parameters such as step or stride length and gait speed. The technical evaluation demonstrates the utility of the information extracted from the videos and the gait parameters in tackling tasks like sex classification and regression of weight and age. Health&Gait facilitates the progression of artificial vision algorithms for automated gait analysis.
PB Nature Research
SN 2052-4463
YR 2025
FD 2025
LK http://hdl.handle.net/10498/36412
UL http://hdl.handle.net/10498/36412
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026