TD‐O4‐01: The Personal and Societal Impact of the Dementia Ambient Care (Dem@care) Multi‐Sensor Remote‐Monitoring Dementia Care System
Bibliographic record
Abstract
The relation of behavioural and cognitive monitoring parameters to dementia-specific patterns can provide a promising and objective approach to dementia assessment given the long-term nature of the measurements. Furthermore, technologies that monitor daily living can enable a person with dementia to remain independent for longer by supporting their health, well-being and safety, while reducing the burden on family/friends and decreasing healthcare costs. The EU FP7-funded research project “Dementia Ambient Care” (Dem@Care), developed an integrated solution for the remote monitoring, diagnosis and support of people with mild cognitive impairment and mild dementia. It investigated the use of multiple wearable (accelerometers, 2D/3D cameras, microphones) and ambient sensors (visual and infrared cameras, sleep sensors) for the recording of daily activities, lifestyle patterns, emotions, and speech, as well as the use of intelligent mechanisms for the assessment of an individual’s condition at diagnosis, and over time in multiple care settings. Feedback was provided to clinicians, and directly to people with dementia and their caregivers. Dem@Care had a positive impact for people with dementia regarding increased independence. They reported a sense of improvement in their subjective quality of life and in the five key domains addressed by the solution; sleep, physical activity, social interaction, activities of daily living and mood. Improvements for the person with dementia translated into improvements for their informal caregivers and in some cases increased independence (related to dementia severity). Clinicians and formal care staff benefited from improved assessment and diagnostic procedures, enhanced ability to make differential diagnoses, and more timely identification of functional, behavioural, and emotional pattern changes. Although difficult to evaluate the longer-term economic and societal outcomes, we suggest that successful attainment of stakeholder’s personal outcomes will, over time, lead to a reduction in healthcare costs and less social isolation for those living with dementia. Dem@Care is shown to have contributed to the advancement of the technical, clinical, and ethical management of dementia care through the innovative use of ICT solutions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".