Bibliographic record
Abstract
Since 2001, the DOMUS Laboratory has been developing cognitive orthotics and telemonitoring systems to support aging in place.Using off-the-shelf sensors, these systems collect and analyze data to provide meaningful feedback to older adults, caregivers, and clinicians.Successfully deployed in real homes and care residences, they enhance autonomy for vulnerable seniors and individuals with cognitive impairments.Their success stems from integrating living labs, participatory design, and interdisciplinary collaboration, ensuring relevant user-centered solutions.However, the diversity of users and living environments requires flexible, tailored implementation and assistance strategies.Through all our projects we experienced that smart-home technologies are often complex, time-consuming, and expert-dependent, requiring expertise in computer science, healthcare, and caregiving-a combination that is rarely accessible in one place at the same time.To overcome this challenge, we are developing a do-it-yourself (DIY) approach, empowering non-experts to create and customize their own assistive solutions.By adopting this approach, caregivers and even individuals with cognitive impairments can actively shape more adaptive, effective, and personalized environments.The resulting DIY platform leverages IoT, ontologies, activity recognition, and augmented reality to create intuitive, customizable smart-home solutions.This presentation highlights three cognitive orthotics developed at DOMUS Lab, demonstrating their applications in daily activity monitoring, meal preparation, and nighttime support.Using these as case studies, we outline the core components of our DIY approach to Ambient Assisted Living (AAL) and explore key challenges such as usability, interdisciplinary integration (technology, health, and design), and system adoption within healthcare networks and society.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".