Feasibility of Applying a Wearable Electrodermal Activity Sensor for Individual Fall Risk Assessment
Bibliographic record
Abstract
Current fall risk assessment approaches that monitor physical movements may have difficulty distinguishing slip, trip, and fall (STF) hazard exposure from transitional walking, such as bending, hopping, and ankle rotation, due to similar motion patterns. To address this issue, the authors examine the feasibility of monitoring physiological arousal by applying a wearable electrodermal activity (EDA) sensor. Twelve subjects’ arousal data were collected in an indoor predetermined route to statistically compare the arousal level difference among regular walking, transitional walking, and STF hazard exposure. We employed the Friedman’s test with the Nemenyi post-hoc test to determine the statistical significance of differences in arousal levels among the three exposures. The result demonstrates a significantly higher mean arousal level following STF hazard exposure compared to regular walking and transitional walking (p < 0.05). This finding suggests that wearable EDA sensing of arousal may feasibly distinguish STF hazard exposure from other walking activities.
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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 | 0.000 |
| Research integrity | 0.001 | 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".