AI-Enabled Smart Glasses for Active Aging: Scoping Review
Bibliographic record
Abstract
Background: The daily use of digital technologies is transforming the day-to-day lives of older adults. Among these technologies, artificial intelligence-enabled smart glasses have recently emerged, which allow constant interaction with the device itself and with the environment. They are designed to be used for multitasking, including options such as being able to take photographs and/or videos; record immersive audio; make calls; and share multimedia content through voice commands, touch, or blink detection. Objective: The aim of this study was to map the existing evidence and gain insight into the effectiveness and potential benefits of artificial intelligence-enabled smart glasses in promoting active living in old age. Methods: A scoping review was performed following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement by consulting the PubMed, Scopus, and Web of Science databases using a search strategy and syntax ("smart glasses" AND ("older adult" OR elderly OR aging) AND health). The review process was conducted through the Covidence online platform, and the final review protocol was prospectively registered in the Open Science Framework. Both the research question and eligibility criteria were based on the population, concept, and context framework for scoping reviews. Results: From a total of 58 papers identified, 6 (10.3%) studies were finally included (2 pilot studies, 2 technological development studies with experimental validation of a prototype, 1 mixed methods feasibility trial, and 1 survey) published between 2015 and 2023 after eliminating duplications and screening titles, abstracts, and keywords. The results suggest that the use of artificial intelligence-enabled smart glasses may contribute to improving the quality of life, independence, autonomy, motor functions, and social interactions of older adults. Conclusions: Because of the novelty of this type of digital technology, there is very little research on this topic at present. Moreover, the adoption and implementation of artificial intelligence-enabled smart glasses are conditioned by hindering factors such as data protection, the high price, and the lack of compatibility with conventional prescription glasses, as well as the lack of evaluation of their effectiveness, usability, and acceptance.
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) | 0.000 | 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 | 0.000 |
| 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".