Emotion expressions and cognitive impairments in the elderly: review of the contactless detection approach
Bibliographic record
Abstract
The aging population in Canada has been increasing continuously throughout the past decades. Amongst this demographic, around 11% suffer from some form of cognitive decline. While diagnosis through traditional means (i.e., Magnetic Resonance Imagings (MRIs), positron emission tomography (PET) scans, cognitive assessments, etc.) has been successful at detecting this decline, there remains unexplored measures of cognitive health that could reduce stress and cost for the elderly population, including approaches for early detection and preventive methods. Such efforts could additionally contribute to reducing the pressure and stress on the Canadian healthcare system, as well as improve the quality of life of the elderly population. Previous evidence has demonstrated emotional facial expressions being altered in individuals with various cognitive conditions such as dementias, mild cognitive impairment, and geriatric depression. This review highlights the commonalities among these cognitive health conditions, and research behind the contactless assessment methods to monitor the health and cognitive well-being of the elderly population through emotion expression. The contactless detection approach covered by this review includes automated facial expression analysis (AFEA), electroencephalogram (EEG) technologies and heart rate variability (HRV). In conclusion, a discussion of the potentials of the existing technologies and future direction of a novel assessment design through fusion of AFEA, EEG and HRV measures to increase detection of cognitive decline in a contactless and remote manner will be presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".