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Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions
Anna Tsiakiri, Spyridon Plakias, Georgios Giarmatzis, Georgia J. Tsakni, Foteini Christidi, Georgia Karakitsiou, Vasiliki Georgousopoulou, Georgios Manomenidis et al.. Sensors, 2025-12-18
Topic: Risk, prevention and care in population studies
The progressive nature of dementia necessitates early detection strategies capable of identifying preclinical cognitive decline. Gait disturbances, mediated by higher-order cognitive functions, have emerged as potential digital biomarkers in this context. This bibliometric review systematically maps the scientific output from 2010 to 2025 on the application of wearable sensor technologies and gait analysis in the early diagnosis of dementia. A targeted search of the Scopus database yielded 126 peer-reviewed studies, which were analyzed using VOSviewer for performance metrics, co-authorship networks, bibliographic coupling, co-citation, and keyword co-occurrence. The findings delineate a multidisciplinary research landscape, with major contributions spanning neurology, geriatrics, biomedical engineering, and computational sciences. Four principal thematic clusters were identified: (1) Cognitive and Clinical Aspects of Dementia, (2) Physical Activity and Mobility in Older Adults, (3) Technological and Analytical Approaches to Gait and Frailty and (4) Aging, Cognitive Decline, and Emerging Technologies. Despite the proliferation of research, significant gaps persist in longitudinal validation, methodological standardization, and integration into clinical workflows. This review emphasizes the potential of sensor-derived gait metrics to augment early diagnostic protocols and advocates for interdisciplinary collaboration to advance scalable, non-invasive diagnostic solutions for neurodegenerative diseases.
Key findings
- Despite the proliferation of research, significant gaps persist in longitudinal validation, methodological standardization, and integration into clinical workflows.
Why it matters
This bibliometric review maps research on wearable sensors and gait analysis for early dementia detection and finds the field is still growing. It reports major gaps in long-term validation, standardized methods, and use in clinical workflows, so gait sensors are not yet an established way to prevent or treat dementia.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 50/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Sensors) |
| Journal's citation record | 3 / 4 | Sensors: journal h-index 338 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| 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 | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 6 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 13.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 6 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Pinelopi Vlotinou: h-index 12 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Aristotle University of Thessaloniki: institution h-index 560 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41471663) |
| Free to read | 2 / 2 | Free to read |
| Total | 50 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Tsiakiri, A., Plakias, S., Giarmatzis, G., Tsakni, G. J., Christidi, F., Karakitsiou, G., et al. (2025). Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions. Sensors. https://doi.org/10.3390/s25247669
Vancouver
Tsiakiri A, Plakias S, Giarmatzis G, Tsakni GJ, Christidi F, Karakitsiou G, et al. Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions. Sensors. 2025. doi:10.3390/s25247669. PMID: 41471663.
BibTeX
@article{tsiakiri2025-41471663,
author = {Tsiakiri, Anna and Plakias, Spyridon and Giarmatzis, Georgios and Tsakni, Georgia J. and Christidi, Foteini and Karakitsiou, Georgia and Georgousopoulou, Vasiliki and Manomenidis, Georgios and others},
title = {{Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions}},
journal = {Sensors},
year = {2025},
doi = {10.3390/s25247669},
pmid = {41471663},
url = {https://doi.org/10.3390/s25247669}
}
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