Understanding Older Users' Acceptance of Wearable Interfaces for Sensor-based Fall Risk Assessment
Why this work is in the frame
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Bibliographic record
Abstract
Algorithms processing data from wearable sensors promise to more accurately predict risks of falling -- a significant concern for older adults. Substantial engineering work is dedicated to increasing the prediction accuracy of these algorithms; yet fewer efforts are dedicated to better engaging users through interactive visualizations in decision-making using these data. We present an investigation of the acceptance of a sensor-based fall risk assessment wearable device. A participatory design was employed to develop a mobile interface providing visualizations of sensor data and algorithmic assessments of fall risks. We then investigated the acceptance of this interface and its potential to motivate behavioural changes through a field deployment, which suggested that the interface and its belt-mounted wearable sensors are perceived as usable. We also found that providing contextual information for fall risk estimation combined with relevant practical fall prevention instructions may facilitate the acceptance of such technologies, potentially leading to behaviour change.
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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 | 0.001 | 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 | 0.001 |
| 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 it