BlanketSet -- A clinical real-world in-bed action recognition and qualitative semi-synchronised MoCap dataset
This addresses a data gap for researchers and practitioners in biomedical computer vision, but it is incremental as it primarily provides a new dataset without novel methodological advancements.
The authors tackled the lack of annotated datasets for clinical in-bed human motion analysis by introducing BlanketSet, an RGB-IR-D action recognition dataset collected in a hospital bed, which aims to transfer improvements from general datasets to clinical scenarios.
Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches lack annotated examples for these clinical scenarios. To address this issue, we introduce BlanketSet, an RGB-IR-D action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in more general large datasets to these clinical scenarios. Information on how to access the dataset is available at https://rdm.inesctec.pt/dataset/nis-2022-004.