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.
Authors
- Benjamin Tilbury
- Miguel Arevalillo-Herráez
- Naeem Ramzan
Institutions
- Universitat de València (ES)
- Artificial Intelligence Research Institute (ES)
- University of the West of Scotland (GB)
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
Funders
- UK Research and Innovation
- Ministerio de Ciencia, Innovación y Universidades
- European Commission
- Agencia Estatal de Investigación