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Gait and movement analysis for discrimination between people with dementia and healthy control persons based on pose estimation and machine learning
Mustafa Al-Hammadi, Hasan A. Fleyeh, Ilias Thomas. Journal of Alzheimer s Disease, 2026-03-15
Topic: Risk, prevention and care in population studies
BackgroundDementia disorders are affecting millions of people globally, characterized by memory loss, communication difficulties, and motor function decline. Accurate and early dementia detection is crucial for effective management and treatment. Gait analysis offers a non-invasive method for dementia detection by identifying subtle changes in walking patterns that often precede cognitive symptoms.ObjectiveThis study aims to evaluate the clinical utility of video-based gait analysis using the Timed Up and Go (TUG) test under single and dual-task conditions (TUGdt) for distinguishing individuals with dementia disorders from healthy controls (HCs).MethodThe study implemented three machine learning models: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), to discriminate between persons with dementia and HCs. The dataset consists of a cohort of 64 people with dementia (47 with Alzheimer's disease) and 67 HCs. The participants performed the TUG test as a single and dual-task (TUGdt). In the TUGdt, participants performed the TUG test while simultaneously completing an additional cognitive task (i.e., animal naming (TUGdt-NA) or reciting months in reverse order (TUGdt-MB)).ResultsThe results showed that dual-task classification outperformed the single-task. The SVM algorithm achieved the highest accuracy in the TUGdt-NA task (accuracy of 87% ± 5.1 and recall of 86.6% ± 3.2) using 5-fold cross-validation and accuracy of 85.5% and recall of 89.5% using Leave-One-Out Cross-Validation (LOOCV) in the TUGdt-MB task.ConclusionsIn summary, video-based gait features effectively distinguish people with dementia from HCs, particularly under dual-tasking, offering cost-effective, automated, and non-invasive pre-screening to complement clinical assessments.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Al-Hammadi, M., Fleyeh, H. A., & Thomas, I. (2026). Gait and movement analysis for discrimination between people with dementia and healthy control persons based on pose estimation and machine learning. Journal of Alzheimer s Disease. https://doi.org/10.1177/13872877261430001
Vancouver
Al-Hammadi M, Fleyeh HA, Thomas I. Gait and movement analysis for discrimination between people with dementia and healthy control persons based on pose estimation and machine learning. Journal of Alzheimer s Disease. 2026. doi:10.1177/13872877261430001. PMID: 41834402.
BibTeX
@article{alhammadi2026-41834402,
author = {Al-Hammadi, Mustafa and Fleyeh, Hasan A. and Thomas, Ilias},
title = {{Gait and movement analysis for discrimination between people with dementia and healthy control persons based on pose estimation and machine learning}},
journal = {Journal of Alzheimer s Disease},
year = {2026},
doi = {10.1177/13872877261430001},
pmid = {41834402},
url = {https://doi.org/10.1177/13872877261430001}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- In the TUGdt, participants performed the TUG test while simultaneously completing an additional cognitive task (i.e., animal naming (TUGdt-NA) or reciting months in reverse order (TUGdt-MB)).ResultsThe results showed that dual-task classification outperformed the single-task.
Why it matters
Video-based gait analysis using the Timed Up and Go test under dual-task conditions distinguished people with dementia from healthy controls, with the best accuracy of 87% in the animal naming dual-task. This is a diagnostic approach and does not address treating, curing, or preventing Alzheimer's disease.
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Study details
- Participants64
- Effect size86.6%; 85.5%; 89.5%
- Evidence strengthModerate
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of Alzheimer s Disease) |
| Journal's citation record | 2 / 4 | Journal of Alzheimer s Disease: journal h-index 218 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 1 / 4 | 64 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 4 / 6 | Cited 15.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Ilias Thomas: h-index 11 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Dalarna University: institution h-index 152 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41834402) |
| Free to read | 2 / 2 | Free to read |
| Total | 43 / 100 | — |
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Read the original
- DOI: 10.1177/13872877261430001
- PubMed: 41834402
- Original source →
- Free full text: Journal of Alzheimer s Disease →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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