Automatic Task Assistance for People with Cognitive Disabilities in Brushing Teeth - A User Study with the TEBRA System
Bibliographic record
Abstract
People with cognitive disabilities such as dementia and intellectual disabilities tend to have problems in coordinating steps in the execution of Activities of Daily Living (ADLs) due to limited capabilities in cognitive functioning. To successfully perform ADLs, these people are reliant on the assistance of human caregivers. This leads to a decrease of independence for care recipients and imposes a high burden on caregivers. Assistive Technology for Cognition (ATC) aims to compensate for decreased cognitive functions. ATC systems provide automatic assistance in task execution by delivering appropriate prompts which enable the user to perform ADLs without any assistance of a human caregiver. This leads to an increase of the user’s independence and to a relief of caregiver’s burden. In this article, we describe the design, development and evaluation of a novel ATC system. The TEBRA (TEeth BRushing Assistance) system supports people with moderate cognitive disabilities in the execution of brushing teeth. A main requirement for the acceptance of ATC systems is context awareness: explicit feedback from the user is not necessary to provide appropriate assistance. Furthermore, an ATC system needs to handle spatial and temporal variance in the execution of behaviors such as different movement characteristics and different velocities. The TEBRA system handles spatial variance in a behavior recognition component based on a Bayesian network classifier. A dynamic timing model deals with temporal variance by adapting to different velocities of users during a trial. We evaluate a fully functioning prototype of the TEBRA system in a study with people with cognitive disabilities. The main aim of the study is to analyze the technical performance of the system and the user’s behavior in the interaction with the system with regard to the main hypothesis: is the TEBRA system able to increase the user’s independence in the execution of brushing teeth?
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".