Skip to main content

Research Repository

Advanced Search

Transformers and human-robot interaction for delirium detection

Jeffcock, Joe; Hansen, Mark; Ruiz Garate, Virginia

Transformers and human-robot interaction for delirium detection Thumbnail


Authors

Joe Jeffcock

Mark Hansen Mark.Hansen@uwe.ac.uk
Professor of Machine Vision and Machine Learning

Virginia Ruiz Garate



Abstract

An estimated 20% of patients admitted to hospital wards are affected by delirium. Early detection is recommended to treat underlying causes of delirium, however workforce strain in general wards often causes it to remain undetected. This work proposes a robotic implementation of the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) to aid early detection of delirium. Interactive features of the assessment are performed by Human-robot Interaction while a Transformer-based deep learning model predicts the Richmond Agitation Sedation Scale (RASS) level of the patient from image sequences; thermal imaging is used to maintain patient anonymity. A user study involving 18 participants role-playing each of alert, agitated, and sedated levels of the RASS is performed to test the HRI components and collect a dataset for deep learning. The HRI system achieved accuracies of 1.0 and 0.833 for the inattention and disorganised thinking features of the CAM-ICU, respectively, while the trained action recognition model achieved a mean accuracy of 0.852 on the classification of RASS levels during cross-validation. The three features represent a complete set of capabilities for automated delirium detection using the CAM-ICU, and the results demonstrate the feasibility of real-world deployment in hospital general wards.

Citation

Jeffcock, J., Hansen, M., & Ruiz Garate, V. (2023). Transformers and human-robot interaction for delirium detection. In 2023 ACM/IEEE International Conference on Human-Robot Interaction (466-474). https://doi.org/10.1145/3568162.3576971

Conference Name HRI '23: Proceedings of 2023 ACM/IEEE International Conference on Human-Robot Interaction
Conference Location Stockholm, Sweden
Start Date Mar 13, 2023
End Date Mar 16, 2023
Acceptance Date Feb 12, 2022
Online Publication Date Mar 13, 2023
Publication Date Mar 13, 2023
Deposit Date Apr 26, 2023
Publicly Available Date Apr 26, 2023
Publisher Association for Computing Machinery (ACM)
Pages 466-474
Book Title 2023 ACM/IEEE International Conference on Human-Robot Interaction
DOI https://doi.org/10.1145/3568162.3576971
Keywords Transformers; human-robot interaction; delirium detection
Public URL https://uwe-repository.worktribe.com/output/10582571
Publisher URL https://dl.acm.org/doi/10.1145/3568162.3576971
Related Public URLs https://dl.acm.org/doi/proceedings/10.1145/3568162

Files

Transformers and human-robot interaction for delirium detection (1.1 Mb)
PDF

Licence
http://www.rioxx.net/licenses/all-rights-reserved

Publisher Licence URL
http://www.rioxx.net/licenses/all-rights-reserved

Copyright Statement
This is the authors accepted manuscript of the article ‘Jeffcock, J., Hansen, M., & Ruiz Garate, V. (2023). Transformers and human-robot interaction for delirium detection. In 2023 ACM/IEEE International Conference on Human-Robot Interaction (466–474)’. DOI: https://doi.org/10.1145/3568162.3576971

The final published version is available here: https://dl.acm.org/doi/10.1145/3568162.3576971




You might also like



Downloadable Citations