A Review of Monitoring Technology for Use With Older Adults
Bibliographic record
Abstract
BACKGROUND: Many Americans are living longer and recent studies report that the majority of older adults wish to live as independently as possible in their own homes, for as long as possible. This creates a need for effective monitoring, and technology has much to offer. PURPOSE: The purpose of this article is to provide a narrative review of current monitoring technology appropriate for older adults' home use. METHODS: A review of current literature provides a comprehensive discussion of the development of this technology. DISCUSSION: In the past several years, advancements have been made in the area of monitoring the movement and activity of older adults in their home environment. This technology may benefit older individuals by assisting them to remain living in their own homes for as long as possible. Unobtrusive monitoring and wearable technology, which is clothing or accessories that incorporate computer or electronic technology, are rapidly expanding areas of development. Various applications of this technology may assist in falls detection and overall safety. In addition, continuous monitoring of an older adult's specific movements or activity can identify changes in day-to-day activity levels, which may indicate a change in medical status. CONCLUSION: Although many devices are still at the developmental stage, the future of this technology may offer older adults a level of increased safety and security in their homes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".