Augmented Reality Technology for People Living with Dementia and their Care Partners
Bibliographic record
Abstract
We designed and implemented an Augmented Reality system, called the My Daily Routine (MDR) system, to demonstrate technology that may aid people living with dementia and their care partners. Although people with dementia are often dependent on their care partners in their daily lives, their independence may be enhanced using Augmented Reality. The MDR system consists of a website and a HoloLens Augmented Reality (AR) application. The care partner can display and customize reminder content using the website. When wearing a Microsoft HoloLens AR device running MDR, a person with dementia will be able to receive personalized reminders in the form of text, images, videos, displayed three dimensional models, voice messages, or music. Customization controls the choice of reminders and their timing; for example, reminders can be issued when an object is detected, at a certain time, or when a command is voiced. MDR can also display the names of common objects and navigation instructions. Through use of the HoloLens’ powerful spatial mapping capabilities and Microsoft’s experimental World Locking Tools, the indoor navigation system in MDR is accurate and easy to set up.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".