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Published on: February 1, 2020
A multimodal dataset of harmful simulated behaviours in high-risk clinical settings using radar
Benjamin Tilbury1, Miguel Arevalillo-Herráez2,3, Naeem Ramzan4
1School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, UK.
Abstract:
We present a new dataset comprising radar, Electrocardiography (ECG), respiration, and inertial measurement signal recordings from 23 individuals while performing a series of simulated harmful behaviors. This dataset covers a range of actions across various levels of agitation and is especially well-suited for conducting research in health monitoring within high-risk clinical settings, such as inpatient psychiatric units. The dataset's design prioritizes unrestricted, naturalistic behavior capture, providing valuable insights into real-world scenarios and supporting a wide range of applications. Although the dataset was initially designed for patient monitoring, the provided ECG and respiration recording extend the potential uses of the data to localization and non-contact vital sign measurement.
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