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Automated assessment of cognitive health using smart home technologies
Dawadi PN, Cook DJ, Schmitter-Edgecombe M, Parsey C.. Technology and health care : official journal of the European Society for Engineering and Medicine, 2013-01-01
Topic: Risk, prevention and care in population studies
Background The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. Objective This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. Methods This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Results Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. Conclusions The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Dawadi, P. N., Cook, D. J., Schmitter-Edgecombe, M., & Parsey, C. (2013). Automated assessment of cognitive health using smart home technologies. Technology and health care : official journal of the European Society for Engineering and Medicine. https://doi.org/10.3233/thc-130734
Vancouver
Dawadi PN, Cook DJ, Schmitter-Edgecombe M, Parsey C. Automated assessment of cognitive health using smart home technologies. Technology and health care : official journal of the European Society for Engineering and Medicine. 2013. doi:10.3233/thc-130734. PMID: 23949177.
BibTeX
@article{dawadi2013-23949177,
author = {Dawadi, P. N. and Cook, D. J. and Schmitter-Edgecombe, M. and Parsey, C.},
title = {{Automated assessment of cognitive health using smart home technologies}},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
year = {2013},
doi = {10.3233/thc-130734},
pmid = {23949177},
url = {https://doi.org/10.3233/thc-130734}
}
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Key findings
- Results Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed.
- With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality.
- Conclusions The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.
Why it matters
This study used smart home sensors and machine learning to predict activity quality and assess cognitive health in 263 volunteers. It found a statistically significant correlation between direct observation scores and predicted activity quality, but this is a monitoring method, not a treatment or cure.
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Study details
- Participants263
- Evidence strengthLow
How this study scored 43/100
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 (Technology and health care : official journal of the European Society for Engineering and Medicine) |
| Journal's citation record | 0 / 4 | Technology and Health Care: journal h-index 64 (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 | 1 / 5 | 2013 — within 20 year(s) of the newest study here |
| Sample size | 2 / 4 | 263 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 108 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.2 citations a year over 15 year(s) since 2013 |
| Impact for its field and year | 4 / 6 | Cited 12.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 3 other studies in this library |
| Still being cited | 2 / 4 | Cited 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Carolyn M. Parsey: h-index 13 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Washington State University: institution h-index 748 (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 23949177) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 43 / 100 | — |
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