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.