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

  • Benjamin Tilbury
  • , Miguel Arevalillo-Herráez
  • , Naeem Ramzan*
  • *Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    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.
    Original languageEnglish
    Article number669
    JournalScientific Data
    Volume13
    Early online date13 Mar 2026
    DOIs
    Publication statusE-pub ahead of print - 13 Mar 2026

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being
    2. SDG 9 - Industry, Innovation, and Infrastructure
      SDG 9 Industry, Innovation, and Infrastructure

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