24‐Hour Optimized Lighting for Persons with Dementia: Technology Development
Bibliographic record
Abstract
Abstract Background 65% of persons with dementia (PWD) suffer from disturbed sleeping patterns and 28% experience vision related falls. Improved lighting has been shown in numerous studies since the 1980s to mitigate these effects. Method Computer code was written to optimize the spectra and intensity of light for vision and non‐vision purposes over a 24‐hour cycle based on off‐the‐shelf LEDs. Hardware was developed to implement the lighting scheme. Feedback was received from practitioners and interested parties. Result The 24‐hr dynamic white lighting exceeded the CIE specifications for vision for seniors with high CRI and low Duv at all times. The light’s correlated colour temperature (CCT) and intensity increased rapidly in the morning and dropped slowly into the afternoon and evening. The lighting schedule was implemented using a designated microcontroller that controlled the intensity of four 5‐m LED strips. Three strips were placed in an upper cove near the ceiling and 1 LED strip (nighttime) in woodwork a floor level. The resulting light was uniform throughout the room and shadow‐free. The ratio of horizontal to vertical illuminance was 20% higher than typical direct lighting systems. The final system was certified to Canadian standards and installed in 6 bedrooms at Ressam Gardens Memory Care Community, and at a few other facilities. The majority of feedback about the system was favorable. Conclusion A practical modular lighting system that delivers 24‐hr dynamic white lighting has been certified to Canadian standards and implemented in a number of locations in Hamilton, ON Canada serving persons with dementia. The majority of feedback has been positive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".