Ambient Assisted Living: Identifying New Challenges and Needs for Digital Technologies and Service Innovation
Bibliographic record
Abstract
BACKGROUND: The ambient assisted living (AAL) market is rapidly becoming fundamental to the delivery of health and social care services for the elderly. Worldwide many different steps have been taken to increase the engagement of older adults with these technologies. Much of this work has focused on the development of novel digital services that increase wellbeing or tackle social challenges. AIM: The aim of the study was to identify and describe the demands for AAL-services from the perspective of older adults. We also examine the challenges and needs of the ambient assisted living market using a needs based approach. METHOD: An exploratory case study was conducted with an aim to capture information about older adults' demands for AAL services. A survey was used to collect the data. The survey study respondents validated the results. RESULTS: The results of the study indicate that the area of AAL needs be studied from a multiple-sided market perspective. Our research suggests there is a need to describe and understand the factors that facilitate or constrain the implementation of services with focus on health and social care. There is also a need to describe and analyze the relationship between policy and practice and its effects on the AAL market. It is necessary to capture expressed demand, to identify market challenges at the macro level and to be able to understand how services should operate and serve older adults in practice. Such research is critical to the development of guidance for policy makers, suppliers and service providers. DISCUSSION: Older adults are asking for intelligent, assistive living solutions that help them to continue to live independent lives and remain socially included in their networks, associations, and communities. The elderly need services that stimulate and maintain their physical and intellectual capital. The development of innovative AAL environments is, however, a complex social process that involves the use and delivery of innovative ICT-based services. The implementation and use of AAL to support older adults involve service providers and elderly consumers. CONCLUSIONS: The results of the study may be of interest to policy makers, entrepreneurs, technology suppliers, service providers and health and social care organizations, who are willing to innovate and influence the development of the AAL market through their choices and decisions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".