ArticleScientific data2026
SiViS: Simulated multi-patient physiological clinical states for advanced vital sign radar monitoring research.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
Continuous, clinically reliable non-contact monitoring of multiple patients' vital signs remains a significant technological challenge in care settings. This paper introduces a systematically constructed radar-based dataset designed to test methods for simultaneous multi-patient vital sign monitoring. Developed with Ostrava University Training Hospital, the dataset includes recordings from two-three advanced medical simulation mannequins (SimMan 3G Plus), emulating a wide range of physiological states-from healthy resting to acute emergencies (apnea, cardiac arrest, severe respiratory distress). We varied sensor geometry (top, frontal, lateral views at 1-4 m, 0°/45° angles) and radar parameters (ADC samples, chirp loops, ramp times, frame rates, gains), yielding over 100 uniquely configured sessions. Preliminary beamforming-based processing achieves mean heart-rate and breathing-rate errors near clinical thresholds (MAE ≈ 6.6 bpm for HR, 1.47 bpm for RR), demonstrating the dataset's utility for developing advanced signal processing and machine-learning pipelines. In keeping with FAIR principles, all data are fully documented and publicly accessible, supporting reproducible research toward noninvasive multi-patient monitoring systems.
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