# BlanketSet -- A clinical real-world in-bed action recognition and qualitative semi-synchronised MoCap dataset

Source: [BlanketSet -- A clinical real-world in-bed action recognition and qualitative semi-synchronised MoCap dataset](<https://www.sota2.com/research/paper/68fa700d1e209131cea48e1a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:00.032Z

Authors: Jo\\~ao Carmona, Tam\\'as Kar\\'acsony, Jo\\~ao Paulo Silva Cunha

Published: 2022-10-07T14:58:27.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.03600>)

[PDF](<https://arxiv.org/pdf/2210.03600.pdf>)