Validation of an algorithm for detecting turning in people with cognitive impairment, considering dementia disease subtype
Key findings suggest that this algorithm can detect turn start and end using an IMU in PwCI in agreement with a reference system (video ratings). Future research should consider the clinical application of turning assessment in PwCI, such as its ability to differentiate dementia disease subtypes to support accurate diagnosis.
Authors
- Alan Thomas (ORCID: https://orcid.org/0000-0002-6667-9533)
- Silvia Del Din (ORCID: https://orcid.org/0000-0003-1154-4751)
- Rana Zia Ur Rehman (ORCID: https://orcid.org/0000-0002-4885-7455)
- Lynn Rochester (ORCID: https://orcid.org/0000-0001-5774-9272)
- Abbie Thompson (ORCID: https://orcid.org/0000-0002-5087-502X)
- Leigh Ryan
- Lisa Alcock (ORCID: https://orcid.org/0000-0002-8364-9803)
- Brook Galna (ORCID: https://orcid.org/0000-0002-5890-1894)
- Ríona Mc Ardle (ORCID: https://orcid.org/0000-0001-7959-3563)
- Emily Dignan
Institutions
- Murdoch University (AU)
- Newcastle upon Tyne Hospitals NHS Foundation Trust (GB)
- Janssen (United Kingdom) (GB)
- NIHR Newcastle Biomedical Research Centre (GB)
- Newcastle University (GB)
Publication Details
- Journal
- Gait & Posture
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1016/j.gaitpost.2025.02.011
- Citations
- 1
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- Engineering and Physical Sciences Research Council