Ambient assisted living technologies to support older adults’ health and wellness: a systematic mapping review
Bibliographic record
Abstract
While the proportion of the Older Adults (OAs) population is growing, this shift raises a challenging question: “How can we support OAs to lead independent and healthy lifestyle?”. Many researchers have been studying Ambient Assisted Living Technologies (or AALTs) over the last three decades to tackle this challenge. However, no literature can provide an overall view of research in the field of AALTs and linkages between technical development and related healthcare needs. Thus, we conducted a systematic mapping review of literature focusing on AALTs (N = 7006) to explore three main research questions: 1) When, where, and how AALTs are studied?; 2) What is the technological maturity level of AALTs used to support a health and wellness, and where were they evaluated and/or implemented?; and 3) To which health and wellness purposes are AALTs deployed? We found several noticeable imbalances in literature and identified some strategies to move this field of investigation further and to bring AALTs applications closer to clinical practice. While research in the area is gradually blossoming, the area mainly leads in only a few countries. Furthermore, the majority of research targeted asymptomatic older adults living at home. We hope this paper will help researchers easily understand what type of research, with whom, and where are available in AALT now. Potential challenges associated with AALTs research are also discussed.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".