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AI-Based Severity Classification of Dementia Using Gait Analysis
Gangmin Moon, Jaesung Cho, Hojin Choi, Yun Jin Kim, Gun‐Do Kim, Seong‐Ho Jang. Sensors, 2025-10-02
Topic: Risk, prevention and care in population studies
This study aims to explore the utility of artificial intelligence (AI) in classifying dementia severity based on gait analysis data and to examine how machine learning (ML) can address the limitations of conventional statistical approaches. The study included 34 individuals with mild cognitive impairment (MCI), 25 with mild dementia, 26 with moderate dementia, and 54 healthy controls. A support vector machine (SVM) classifier was employed to categorize dementia severity using gait parameters. As complexity and high dimensionality of gait data increase, traditional statistical methods may struggle to capture subtle patterns and interactions among variables. In contrast, ML techniques, including dimensionality reduction methods such as principal component analysis (PCA) and gradient-based feature selection, can effectively identify key gait features relevant to dementia severity classification. This study shows that ML can complement traditional statistical analyses by efficiently handling high-dimensional data and uncovering meaningful patterns that may be overlooked by conventional methods. Our findings highlight the promise of AI-based tools in advancing our understanding of gait characteristics in dementia and supporting the development of more accurate diagnostic models for complex or large datasets.
Key findings
- Our findings highlight the promise of AI-based tools in advancing our understanding of gait characteristics in dementia and supporting the development of more accurate diagnostic models for complex or large datasets.
Why it matters
A support vector machine classifier used gait parameters to categorize dementia severity in a small sample of 34 people with mild cognitive impairment, 25 with mild dementia, 26 with moderate dementia, and 54 healthy controls. The findings highlight the promise of AI-based tools for diagnosis, not for treating, curing, or preventing Alzheimer's disease.
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Study details
- Participants34
- Evidence strengthLow
How this study scored 44/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported 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 | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” 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 | 1 / 4 | 34 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 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 | 3 / 6 | Cited 3.8× 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 | 1 / 4 | Cited 6 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Seong‐Ho Jang: h-index 28 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Hanyang University: institution h-index 548 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41094906) |
| Free to read | 2 / 2 | Free to read |
| Total | 44 / 100 | — |
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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. Check the citation against the original before you use it.
APA 7th
Moon, G., Cho, J., Choi, H., Kim, Y. J., Kim, G., & Jang, S. (2025). AI-Based Severity Classification of Dementia Using Gait Analysis. Sensors. https://doi.org/10.3390/s25196083
Vancouver
Moon G, Cho J, Choi H, Kim YJ, Kim G, Jang S. AI-Based Severity Classification of Dementia Using Gait Analysis. Sensors. 2025. doi:10.3390/s25196083. PMID: 41094906.
BibTeX
@article{moon2025-41094906,
author = {Moon, Gangmin and Cho, Jaesung and Choi, Hojin and Kim, Yun Jin and Kim, Gun‐Do and Jang, Seong‐Ho},
title = {{AI-Based Severity Classification of Dementia Using Gait Analysis}},
journal = {Sensors},
year = {2025},
doi = {10.3390/s25196083},
pmid = {41094906},
url = {https://doi.org/10.3390/s25196083}
}
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