Bibliographic record
Abstract
At DOMUS for the past 14 years, several cognitive orthotics were designed and implemented and evaluated, most of the time using participatory design. The resulting set of orthotics can support a wide variety of activities of daily living (ADL) to foster autonomy at home for people with cognitive impairments, e.g. medication, meal preparation, or budget. DOMUS benefits from a rich and versatile research infrastructure to design, implement, and evaluate such orthotics, namely a smart apartment on the campus, a living lab in an alternative housing unit for people with traumatic brain injury (TBI), seniors' residences, personal residences (apartments and houses). To different extents, all these places can be considered as living labs. In this paper, building on our extensive experience, we first show that participative design is the best-suited methodology for developing cognitive orthotics in living labs. Clinical researchers, caregivers, and end users are involved from the start. This ensures that design is user driven and that assistive technologies will satisfy users needs. Then we propose a classification of living labs according to the levels of control one can have on which and how much technology is deployed, on how space is organized and how it may vary from experiment to experiment, and on how the progression and execution of a scenario can be constrained or not. For each category of living labs, we discuss their different yet complementary characteristics, highlighting their pros and cons. For instance, our smart apartment provides tight control over technology, space, and execution of predefined scenarios. Accordingly evaluations then provide a lot of useful and reliable information about technology and the ability for people to use it, but less on acceptance of the technology and its integration at one's real home in her daily life habits.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".