Skip to main content

Nursing Activity Recognition for Automated Care Documentation in Clinical Settings

  • Conference paper
  • First Online:
Artificial Intelligence XLI (SGAI 2024)

Abstract

This paper introduces a concept for an assistance system that enables nursing staff in real-life clinical settings to reduce the time-consuming nursing documentation effort, e.g. from the morning routine. The overall goal is an AI-based documentation assistant that pre-fills the documentation record. An essential constraint in collecting and processing data is the use of sensors and features that preserve people’s privacy and the acceptance of being observed.

The selected sensors are known body-worn acceleration sensors as well as far-infrared based thermal scans. During the training and evaluation phase, the use of an Azure Kinect is foreseen as well. A crucial intermediate step within the AI based concept is the autonomous identification and learning of actions that form an activity that is recognised as a part of the entire routine added to the documentation. Both open data sets and self-recorded material tailored to the use case are to be used for this purpose. By checking the automatically generated documentation results after each treatment by the nursing staff, additional training material is to be constantly generated during the application operation in order to improve the pattern recognition systems in the processing layer in the long term.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bertram, J., et al.: Accuracy and repeatability of the microsoft azure Kinect for clinical measurement of motor function. PloS One 18, e0279697 (2023). https://doi.org/10.1371/journal.pone.0279697

  2. Bruns, F.T., Pauls, A., Koppelin, F., Wallhoff, F.: Activity recognition of nursing tasks in a hospital: requirements and challenges. In: Salvi, D., Van Gorp, P., Shah, S.A. (eds.) PH 2023. LNICS, Social Informatics and Telecommunications Engineering, vol. 572, pp. 235–243. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-59717-6_16

  3. Dall’Ora, C., Ball, J., Reinius, M., Griffiths, P.: Burnout in nursing: a theoretical review. Hum. Resour. Health 18(1), 41 (2020). https://doi.org/10.1186/s12960-020-00469-9

  4. Demrozi, F., Turetta, C., Machot, F.A., Pravadelli, G., Kindt, P.H.: A comprehensive review of automated data annotation techniques in human activity recognition (2023). https://doi.org/10.48550/ARXIV.2307.05988

  5. Ijaz, M., Diaz, R., Chen, C.: Multimodal transformer for nursing activity recognition. In: Proceedings: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2064–2073 (2022). https://doi.org/10.1109/CVPRW56347.2022.00224

  6. Inoue, S., Alia, S.S., Lago, P., Goto, H., Takeda, S.: Nurse care activities datasets: in laboratory and in real field (2020). https://doi.org/10.21227/jem3-ap07

  7. Joukes, E., Abu-Hanna, A., Cornet, R., de Keizer, N.F.: Time spent on dedicated patient care and documentation tasks before and after the introduction of a structured and standardized electronic health record. Appl. Clin. Inform. 09(01), 046–053 (2018). https://doi.org/10.1055/s-0037-1615747

    Article  Google Scholar 

  8. Kaufmann, T., Weng, P., Bengs, V., Hüllermeier, E.: A survey of reinforcement learning from human feedback (2024). https://arxiv.org/abs/2312.14925

  9. Okuda, R., Xia, Q., Maekawa, T., Hara, T., Inoue, S.: Activity prediction method for nursing care records with missing entries. Int. J. Act. Behav. Comput. (2024)

    Google Scholar 

Download references

Acknowledgements

This study was supported by the Lower Saxony Ministry for Science and Culture with funds from the governmental funding initiative zukunft.niedersachsen of the Volkswagen Foundation, project “Data-driven health (DEAL)”.

The experiment complied with the Declaration of Helsinki and was approved by the ethics committee of the University of Oldenburg with approval identifier Drs.EK/2024/027.

Furthermore the authors would like to express their thanks to the undergraduate student Fabian Kessener for building the training database.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Frank Wallhoff .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wallhoff, F., Hesselmann, F.T. (2025). Nursing Activity Recognition for Automated Care Documentation in Clinical Settings. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XLI. SGAI 2024. Lecture Notes in Computer Science(), vol 15447. Springer, Cham. https://doi.org/10.1007/978-3-031-77918-3_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-77918-3_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77917-6

  • Online ISBN: 978-3-031-77918-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics