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Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults
Open accessAbstract
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.
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Passive infrared motion sensors in homes can identify MCI through movement trajectories, whereas speech analytics and handwriting dynamics provide clinically relevant indicators.
“Passive infrared motion sensors in homes can identify MCI through movement trajectories (e.g. home-based daily walking speeds and their variability) (Akl et al., 2015), whereas speech analytics and handwriting dynamics provide clinically relevant indicators (Ashraf and Taati, 2015; Tóth et al., 2018; Angelillo et al., 2019).”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Akl et al., 2015
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References 35 · Cited by 126
Research works printed but not linked: 3
- 2011, GE Healthcare
- 2010, Alzheimer society of B C
- kirby, 2002, The health of canadians—The federal role, volume two: Current trends and future challenges, The Standing Senate Committee on Social Affairs Science and Technology Tech Rep
Metadata
- Authors
- Akl, A., Taati, B., & Mihailidis, A.
- Affiliations
- University of Toronto (CA)
- Toronto Rehabilitation Institute (CA)
- Corresponding author
- Akl, Ahmad
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2015-05
- Volume
- 62
- Issue
- 5
- Pages
- 1383-1394
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- PMC
- PMC4406793
- OpenAlex
- W2001215426
- Cited by
- 126
- References
- 35
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 7 · 2023: 7 · 2022: 23 · 2021: 17 · 2020: 15 · 2019: 7 · 2018: 9 · 2017: 10 · 2016: 9 · 2015: 2
- FWCI
- 8.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Oregon Roybal Center for Translational Research on Aging (P30AG024978)
- Oregon Alzheimer's Disease Center (P30AG008017)
- BRP (R01AG024059)
- SAAC Intelligent Systems for Assessing Aging Changes, and Intel Corporation BRP
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- mild cognitive impairment, early detection, older adults, unobtrusive sensing, home monitoring, walking speed, signal processing, support vector machine, random forest, dementia
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Artificial Intelligence, Female, Humans, Male, ROC Curve, Signal Processing, Computer-Assisted, Remote Sensing Technology, Support Vector Machine, Cognitive Dysfunction
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