ePAD: Engaging Platform for Art Development
Bibliographic record
Abstract
We present a class of devices for use by art thera-pists working with older adults with a progressive illness such as Alzheimer’s disease. We call these devices ePADs. An ePAD combines a touch-screen interface with intelligent user modeling and sens-ing through cameras using computer vision. Us-ing a probabilistic model, an ePAD monitors the behaviours of a user as well as aspects of their af-fective or internal state, including their responsive-ness and engagement with the device. The ePAD then uses decision theoretic planning to enable sit-uated, adaptive strategies for interaction with a hu-man user. In this paper, we discuss results and anal-ysis of a survey of arts therapists, and of one-on-one interviews. We then give details of the ePAD class, framed as a partially observable Markov de-cision process, or POMDP. A key element of this class is that instantiations can be easily made for a wide range of customisable devices and interface applications for art-making moderation. We show examples of particular instances of this model on three devices and with three different interfaces. We give laboratory demonstrations of the function-ality of the devices, and we present and discuss our next steps, including end user testing. 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".