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https://doi.org/10.21256/zhaw-27810
Publikationstyp: | Konferenz: Paper |
Art der Begutachtung: | Peer review (Publikation) |
Titel: | Video object detection for privacy-preserving patient monitoring in intensive care |
Autor/-in: | Emberger, Raphael Boss, Jens Michael Baumann, Daniel Seric, Marko Huo, Shufan Tuggener, Lukas Keller, Emanuela Stadelmann, Thilo |
et. al: | No |
DOI: | 10.1109/SDS57534.2023.00019 10.21256/zhaw-27810 |
Tagungsband: | 2023 10th IEEE Swiss Conference on Data Science (SDS) |
Seite(n): | 85 |
Seiten bis: | 88 |
Angaben zur Konferenz: | 10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023 |
Erscheinungsdatum: | Jun-2023 |
Verlag / Hrsg. Institution: | IEEE |
ISBN: | 979-8-3503-3875-1 |
Sprache: | Englisch |
Schlagwörter: | Object recognition; Medical informatics; Data-centric AI; DCAI |
Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
Zusammenfassung: | Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support systems. These systems, however, are vulnerable to artifacts (e.g. muscle movement due to ongoing treatment), which are often indistinguishable from real and potentially dangerous signals. Video recordings could facilitate the reliable classification of biosignals using object detection (OD) methods to find sources of unwanted artifacts. Due to privacy restrictions, only blurred videos can be stored, which severely impairs the possibility to detect clinically relevant events such as interventions or changes in patient status with standard OD methods. Hence, new kinds of approaches are necessary that exploit every kind of available information due to the reduced information content of blurred footage and that are at the same time easily implementable within the IT infrastructure of a normal hospital. In this paper, we propose a new method for exploiting information in the temporal succession of video frames. To be efficiently implementable using off-the-shelf object detectors that comply with given hardware constraints, we repurpose the image color channels to account for temporal consistency, leading to an improved detection rate of the object classes. Our method outperforms a standard YOLOv5 baseline model by +1.7% mAP@.5 while also training over ten times faster on our proprietary dataset. We conclude that this approach has shown effectiveness in the preliminary experiments and holds potential for more general video OD in the future. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/27810 |
Volltext Version: | Akzeptierte Version |
Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
Departement: | School of Engineering |
Organisationseinheit: | Centre for Artificial Intelligence (CAI) |
Publiziert im Rahmen des ZHAW-Projekts: | AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support |
Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
2023_Emberger-etal_Video-OD-for-privacy-preserving-patient-monitoring_SDS.pdf | Accepted Version | 2.89 MB | Adobe PDF | ![]() Öffnen/Anzeigen |
Zur Langanzeige
Emberger, R., Boss, J. M., Baumann, D., Seric, M., Huo, S., Tuggener, L., Keller, E., & Stadelmann, T. (2023). Video object detection for privacy-preserving patient monitoring in intensive care [Conference paper]. 2023 10th IEEE Swiss Conference on Data Science (SDS), 85–88. https://doi.org/10.1109/SDS57534.2023.00019
Emberger, R. et al. (2023) ‘Video object detection for privacy-preserving patient monitoring in intensive care’, in 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 85–88. Available at: https://doi.org/10.1109/SDS57534.2023.00019.
R. Emberger et al., “Video object detection for privacy-preserving patient monitoring in intensive care,” in 2023 10th IEEE Swiss Conference on Data Science (SDS), Jun. 2023, pp. 85–88. doi: 10.1109/SDS57534.2023.00019.
EMBERGER, Raphael, Jens Michael BOSS, Daniel BAUMANN, Marko SERIC, Shufan HUO, Lukas TUGGENER, Emanuela KELLER und Thilo STADELMANN, 2023. Video object detection for privacy-preserving patient monitoring in intensive care. In: 2023 10th IEEE Swiss Conference on Data Science (SDS). Conference paper. IEEE. Juni 2023. S. 85–88. ISBN 979-8-3503-3875-1
Emberger, Raphael, Jens Michael Boss, Daniel Baumann, Marko Seric, Shufan Huo, Lukas Tuggener, Emanuela Keller, and Thilo Stadelmann. 2023. “Video Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care.” Conference paper. In 2023 10th IEEE Swiss Conference on Data Science (SDS), 85–88. IEEE. https://doi.org/10.1109/SDS57534.2023.00019.
Emberger, Raphael, et al. “Video Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care.” 2023 10th IEEE Swiss Conference on Data Science (SDS), IEEE, 2023, pp. 85–88, https://doi.org/10.1109/SDS57534.2023.00019.
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