AAL for independent aging: Practical guidelines for smart living environment development
Bibliographic record
Abstract
Abstract Background Active Assisted Living (AAL) refers to the use of IoT devices to support quality of life, independence, and healthier living for care recipients. AAL-enabled smart homes have particular potential to help older adults reach their health and independent living goals, but there is a dearth of guidance on practical implementation. Additionally, different technology companies have each developed their own practices, leading to confusion and inconsistency. The objective of this work is to explore requirements for use of AAL in smart living environments for older adults, providing suggestions for best practice for AAL use considering their unique circumstances. Methods A review of academic and grey literature was performed to identify existing best practices and gaps for development of AAL-enabled environments. A technology review was also performed on 156 unique devices to understand the AAL tech ecosystem. Results Little guidance exists regarding designing smart living environments for older adults, though it exists for the two elements separately. The distinguishing elements are how AAL may connect the older adult to their care network, how the home may accommodate changing health needs, and older adults’ unique vulnerability. Furthermore, a large issue is a lack of interoperability and communication, which must be addressed in order to make AAL systems as low-effort and aligned with older adults’ preferences as possible. Conclusions As both tech and older adults’ needs evolve, key requirements of AAL-enabled smart living environments are robust data sharing pathways, planning for modifiability, and protecting the privacy of residents. The findings of this work will be used to identify opportunities for AAL standards development. Key messages • AAL-enabled smart homes must be able to accommodate an older adults’ shifting care needs and preferences over time. • It is necessary to establish how AAL smart home data may be shared with and used by care partners and providers.
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 | 0.025 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".