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Autonomous unobtrusive detection of mild cognitive impairment in older adults
Akl A, Taati B, Mihailidis A.. IEEE transactions on bio-medical engineering, 2015-01-09
Topic: Risk, prevention and care in population studies
The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing approach equipped with a machine learning paradigm to process and analyze real-world data acquired using home-based unobtrusive sensing technologies. Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of three years, a number of measures associated with the subjects' walking speed and general activity in the home were calculated. Different time spans of these measures were used to generate feature vectors to train and test two machine learning algorithms namely support vector machines and random forests. We were able to autonomously detect MCI in older adults with an area under the ROC curve of 0.97 and an area under the precision-recall curve of 0.93 using a time window of 24 weeks. This study is of great significance since it can potentially assist in the early detection of cognitive impairment in older adults.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Akl, A., Taati, B., & Mihailidis, A. (2015). Autonomous unobtrusive detection of mild cognitive impairment in older adults. IEEE transactions on bio-medical engineering. https://doi.org/10.1109/tbme.2015.2389149
Vancouver
Akl A, Taati B, Mihailidis A. Autonomous unobtrusive detection of mild cognitive impairment in older adults. IEEE transactions on bio-medical engineering. 2015. doi:10.1109/tbme.2015.2389149. PMID: 25585407.
BibTeX
@article{akl2015-25585407,
author = {Akl, A. and Taati, B. and Mihailidis, A.},
title = {{Autonomous unobtrusive detection of mild cognitive impairment in older adults}},
journal = {IEEE transactions on bio-medical engineering},
year = {2015},
doi = {10.1109/tbme.2015.2389149},
pmid = {25585407},
url = {https://doi.org/10.1109/tbme.2015.2389149}
}
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Key findings
- Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of three years, a number of measures associated with the subjects' walking speed and general activity in the home were calculated.
Why it matters
Using sensors placed in the home to measure walking speed and general activity, a machine learning approach was able to detect mild cognitive impairment in older adults. This is a detection method only and does not treat, cure or prevent Alzheimer's disease.
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Study details
- Participants97
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, so that was left out: it earned 46 of the 96 points we could judge it on, which is 48 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE transactions on bio-medical engineering) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 1 / 5 | 2015 — within 20 year(s) of the newest study here |
| Sample size | 1 / 4 | 97 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 | 5 / 7 | Cited 126 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 9.7 citations a year over 13 year(s) since 2015 |
| Impact for its field and year | 3 / 6 | Cited 7.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 2 / 4 | Cited 26 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Alex Mihailidis: h-index 53 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Toronto: institution h-index 1,923 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 25585407) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 46 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 46 of the 96 points we could judge it on is 48 out of 100. |
| Total | 48 / 100 | — |
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