Wearable-based estimation of continuous 3D knee moments during running using a convolutional neural network
Bibliographic record
Abstract
This study aimed to develop and validate a machine learning method to estimate continuous 3D knee moments during running from wearable sensor data. Reference knee moments were calculated from 19 recreational runners during treadmill running at varying slopes (0 ± 5 % incline), speeds (self-selected ± 1 km/h) and in 3 types of footwear. A convolutional neural network was trained on data from 7 inertial measuring units (feet, shanks, thighs, sacrum) and a pair of pressure insoles. We assessed performance over continuous time windows (CONT) and during stance phases (PHSS) by intraclass-correlation (ICC), normalised root mean squared error (nRMSE), and statistical parametric mapping. The agreement levels in the sagittal plane were good to excellent (ICC: 0.84-0.98), with low errors (nRMSE: 0.05-0.11). However, accuracy was lower for non-sagittal estimations (frontal ICC: 0.19-0.90, nRMSE: 0.08-0.23; transverse ICC: 0.72-0.94, nRMSE: 0.07-0.17). Accuracy decreased across all planes during PHSS. The proposed approach yields similar or better accuracy compared to previous work while requiring less preprocessing. It provides a viable method for wearable-based assessment of running kinetics in near real-time. Additional data and methods to address inter-individual variability could improve its precision in assessing frontal plane injury risk factors.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".