DESIGN, IMPLEMENTATION AND EVALUATION OF A SMART TOOTHBRUSH FOR INDIVIDUALS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Dementia in general and Alzheimer’s Disease in particular, is one of the most challenging health conditions in our century. Experiencing cognitive decline, resulting in relying on others for daily living activities and impairment in basic mental tasks are the main symptoms. There are yet no proven treatments to slow or prevent neurodegenerative dementia progression; thus, dementia patients eventually need full-time care. To help patients to stay in their own homes longer and ease the caregiver burden, Smart Assistive Technology (SAT) products may be beneficial. One of the basic activities that Alzheimer’s patients in particular need help with, are basic hygiene needs such as brushing their teeth. A smart toothbrush has been designed and implemented as a pilot study towards development of a SAT for basic hygiene functions of dementia patients, while it can also have educational application for children. The design includes hardware and software. The hardware includes 9-Axis motion sensor, individually addressable light-emitting diodes (LEDs), Bluetooth Light Energy (BLE) communication module, laser distance sensor and other electrical components. The software includes real-time monitoring of several dependent and independent tasks using an algorithm to assist the users and transmit that data to a smartphone application. The real-time monitoring system of the designed prototype assists the users by visual and auditory means. It is anticipated the designed prototype will assist people with dementia, and hopefully prolong the time they can be cared for at home; it may also be used for oral hygiene education and instruction in general.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".