Abstract
Introduction:
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.
In order to preserve privacy, all original faces in the videos have been replaced by a deep-fake variation of the original created by the algorithm given in the paper 'DeepPrivacy'. Each frame has an independent new face and as a consequence, there is some flickering on the faces in the videos. The original 2D joint positions are estimated on the original videos using a lightweight implementation of the algorithm given in the paper 'HigherHRNet'.
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.
In order to preserve privacy, all original faces in the videos have been replaced by a deep-fake variation of the original created by the algorithm given in the paper 'DeepPrivacy'. Each frame has an independent new face and as a consequence, there is some flickering on the faces in the videos. The original 2D joint positions are estimated on the original videos using a lightweight implementation of the algorithm given in the paper 'HigherHRNet'.
| Date made available | 15 Jan 2025 |
|---|---|
| Publisher | Edinburgh DataShare |
| Geographical coverage | UK,UNITED KINGDOM |