Preliminary Investigation of Predicting Time-to-Next Heelstrike Using Accelerometers and Machine Learning
Bibliographic record
Abstract
Osteoarthritis knee braces require large brace-leg interface forces to stabilize and unload the joint during weight bearing. Actively removing support while the user is in a non-weight-bearing state could improve the comfort of the brace but requires the timing of weight-bearing states to be known. This study presents two artificial neural networks (ANNs) for predicting time-to-next heelstrike during walking using only data from two accelerometers placed on the thigh and shank. One ANN used teacher forcing and the other did not. Walking data were collected from 10 subjects and leave-one-subject-out cross-validation was used to evaluate the performance of the two models. Input features for the ANNs included tibial and femoral accelerations, concatenated into one array. The teacher forcing ANN and the non-teacher forcing ANN performed equally well (RMSE = 0.23 +/- 0.13s for the non-teacher forcing ANN, RMSE = 0.27 +/- 0.08s for the teacher forcing ANN). The performances of the models were worse than those of previously published studies that predicted heelstrike events. Accelerations were insufficient for an ANN to predict time-to-next heelstrike during walking.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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".