# Video object detection for privacy-preserving patient monitoring in intensive care Source: [Video object detection for privacy-preserving patient monitoring in intensive care]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:00.679Z Authors: Raphael Emberger, Jens Michael Boss, Daniel Baumann, Marko Seric, Shufan Huo, Lukas Tuggener, Emanuela Keller, Thilo Stadelmann (1 and 4) \_\_INSTITUTION\_8\_\_ Centre for Artificial Intelligence, ZHAW School of Engineering, Winterthur, Switzerland, (2) Neurocritical Care Unit, Department of Neurosurgery, Institute of Intensive Care Medicine, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland, (3) Neurology, Charit\\'e - University Medicine Berlin, Berlin, Germany, (4) European Centre for Living Technology \_\_INSTITUTION\_12\_\_, Ca' Bottacin, Venice, Italy) Published: 2023-06-26T11:52:22.000Z ## Abstract 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. ## 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]() [PDF]()