WeightGait Dataset - School of Informatics 2025

Here we introduce the WeightGait dataset: a dataset developed for the purposes of facilitating vision-based gait assessment methodologies with more realistic conditions comparable to real world use. The motivation for this dataset is to create a testing environment for gait assessment algorithms that is closer to the realities of application. To accomplish this, unlike other similar datasets, we do two main things uniquely: We simulate overlapping abnormalities, for a total of 9 different combinations of abnormality detailed below. The background and equipment used are imperfect and noisy to simulate the similar hardship experienced when trying to install a gait monitor into someone's home. This means cheap recording equipment for scalability resulting in relatively low-frames per recording. It also means slight feet/head clipping at times, only a single camera view to detect depth and no curation to the background or the clothing/walking speed of the participants. The original 2D joint positions are estimated on the original videos using a lightweight implementation of the algorithm given in the paper 'HigherHRNet'.