Multi-modal detection of acute fear of falling in older adults: A proof-of-concept study
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Bibliographic record
Abstract
<ns3:p>Fear of falling in older adults is generally studied as a chronic condition, yet spikes in acute fear of falling remain underexplored, despite previous research showing that they often precipitate falls. This work introduces a multi-modal sensor-based framework for detecting theoretically defined 'potential fear of falling' by combining gaze elevation and heart rate signals captured from wearable eye tracking and wrist devices in older adults living in the community. We trained five conventional classifiers (logistic regression, KNN, random forest, XGBoost, CatBoost), optimized for minority class F1, and combined two ensembles: (1) random forest + CatBoost + KNN and (2) random forest + logistic regression + KNN. We also applied spectrogram-based transfer learning by fine-tuning the pre-trained VGG16 and ResNet50 models on accelerometer data. In the individual-classifier analysis, XGBoost, KNN, and random forest achieved ROC AUC = 0.99 and minority-class F1 of 0.93, 0.90, and 0.85, respectively. The ensemble models performed better than individual classifiers on multi-modal and accelerometer-only inputs, though overall performance remained modest without multi-modal signals in the latter case (minority-class F1 = 0.39). Transfer models outperformed ensembles. These results demonstrate that ensemble and spectrogram-based transfer learning models provide robust, high-sensitivity detection of potential acute fear of falling in multi-modal signals. This work lays the foundation for future studies to explore acute fear of falling biomarkers in larger cohorts and paves the way for personalized fall prevention interventions in everyday settings.</ns3:p>
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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