Automated Classification of the Consequences of a Slip During Walking Using a Machine Learning Approach
Bibliographic record
Abstract
This study applied decision-tree (DT) machine learning models to determine whether this approach is more accurate when classifying slip outcome during walking, and to refine the cutoff thresholds of each slip type. Kinematic data of the heel were collected from 50 adults (23.1 [3.6] y) during 516 walking trials. The first DT model (DT1) was trained with heel slip distance and heel slip velocity as predictor variables; the second model (DT2) added heel slip acceleration as the third predictor variable. Walking trials were first classified as a no-slip, slip-recovery, or slip-fall outcome based on visual observation, and these classifications were used as response labels to train the DT models. Results indicated that both DT models yielded different thresholds in classifying slip outcomes and were similar to thresholds suggested in previous studies. However, both DT models resulted in 4.1% to 7.6% greater overall prediction accuracy compared with previously suggested thresholds, with DT2 generally performing better than DT1. Although the improved performance was offset by a ∼7% lower sensitivity when classifying no-slip outcomes and greater model complexity, future studies examining slip responses during gait should incorporate the thresholds derived from the DT2 model to most accurately classify the type of slip outcome.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".