A machine learning model to detect falls mimicking cardiac arrest-related collapse based on wrist-derived accelerometry: the DETECT-2 study
Using accelerometry data from the CardioWatch, sudden and soft falls that mimic cardiac arrest-related collapse can be accurately detected. The next step in the development of automated cardiac arrest detection is the integration of accelerometer signals into the existing PPG-based model, with the aim of reducing false positives and increasing sensitivity in everyday use.
Authors
- Marc A. Brouwer (ORCID: https://orcid.org/0000-0001-8617-7672)
- Alexander M Griffioen (ORCID: https://orcid.org/0000-0001-7110-2934)
- Roos Edgar
- Job J. Herrmann (ORCID: https://orcid.org/0000-0001-5923-3720)
- Judith Bonnes (ORCID: https://orcid.org/0000-0002-4763-7805)
- Kambiz Ebrahimkheil
- Eric Boersma (ORCID: https://orcid.org/0000-0003-1159-7802)
- Peter Stas
- Danny Meeuwsen
- N Van Royen
- Niels T. B. Scholte
- Eelko Ronner
- Rypko J Beukema
- Maud C de Jong
Institutions
- University Medical Center Groningen (NL)
- Radboud University Nijmegen (NL)
- Erasmus MC (NL)
- Radboud University Medical Center (NL)
- Physiotherapy New Zealand (NZ)
- Reinier de Graaf Hospital (NL)
- Wilhelmina Children's Hospital (NL)
- Erasmus University Rotterdam (NL)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2026-03-04
- DOI
- https://doi.org/10.1093/ehjdh/ztag043
- Primary Topic
- Cardiac Arrest and Resuscitation
- Type
- article
- Field-Weighted Citation Impact
- 0.00