High-Accuracy Indoor Positioning and Smart Home Technologies for Assessing and Monitoring Frailty in Older Adults
Bibliographic record
Abstract
Frailty assessment and monitoring are essential for supporting independent living and preventing adverse outcomes among older adults. This study aimed to develop and evaluate the concurrent validity of a high-accuracy home-monitoring system for assessing and tracking frailty in older adults. The system integrated off-the-shelf, zero-effort technologies, including ultra-wideband (UWB) indoor positioning, a smart scale, a connected hand dynamometer, and a Bluetooth speakerphone, to measure the five components of Fried's Frailty Phenotype criteria. Twenty-one participants (aged 21-90 years) completed frailty assessments using both traditional clinical measures and the sensor-based system within a simulated home environment within a major rehabilitation hospital. The developed system demonstrated very strong and statistically significant correlations between the sensor-based system and the Fried's Frailty Phenotype criteria, strong correlations with the Clinical Frailty Scale, and moderate-to-strong correlations with the Edmonton Frailty Scale, confirming the system's strong concurrent validity. These findings indicate that high-accuracy, home-based monitoring technologies can provide reliable, objective, and non-invasive assessment of frailty in older adults, supporting early detection and continuous monitoring. This approach shows promise for future integration into smart home environments to enhance proactive frailty management and aging-in-place strategies.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".