A multimodal dataset of harmful simulated behaviours in high-risk clinical settings using radar

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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Publication Details

Journal
Scientific Data
Published
2026-03-13
DOI
https://doi.org/10.1038/s41597-026-06703-8
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
0.00

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article

A multimodal dataset of harmful simulated behaviours in high-risk clinical settings using radar

Benjamin Tilbury, Miguel Arevalillo-Herráez, Naeem Ramzan
Scientific Data
Non-Invasive Vital Sign Monitoring
article

A multimodal dataset of harmful simulated behaviours in high-risk clinical settings using radar

Benjamin Tilbury, Miguel Arevalillo-Herráez, Naeem Ramzan
article en

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.

Scientific Data
Universitat de València (ES), Artificial Intelligence Research Institute (ES), University of the West of Scotland (GB)
UK Research and Innovation, Ministerio de Ciencia, Innovación y Universidades, European Commission, Agencia Estatal de Investigación
Peace, Justice and strong institutions
Openalex Percentile: Top 26%
Non-Invasive Vital Sign Monitoring
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