Zafra-Palma Jorge, Marín-Jiménez Nuria, Castro-Piñero José, Cuenca-García Magdalena, Muñoz-Salinas Rafael, Marín-Jiménez Manuel J
University of Cordoba, Department of Computing and Numerical Analysis, Córdoba, 14071, Spain.
Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, 14004, Spain.
Sci Data. 2025 Jan 10;12(1):44. doi: 10.1038/s41597-024-04327-4.
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.
获取步态指标和人体测量数据对于评估个人的身体状况至关重要。自动化这一评估过程可减轻医疗保健专业人员的负担并加速患者监测。当前的自动化技术依赖于特定的、昂贵的系统，如OptoGait或MuscleLAB，这些系统需要培训和物理空间。一种更易于使用的替代方案可能是可通过移动设备操作的人工视觉系统。本文介绍了Health&Gait，这是第一个用于基于视频的步态分析的数据集，包含398名参与者和1564个视频。该数据集提供了诸如参与者的轮廓、语义分割、光流和人体姿态等信息。此外，每个参与者的数据包括其性别、身高和体重等人体测量数据，以及步长或步幅长度和步态速度等步态参数。技术评估证明了从视频中提取的信息和步态参数在处理性别分类以及体重和年龄回归等任务中的效用。Health&Gait推动了用于自动步态分析的人工视觉算法的发展。
