Enhancing ML Model Generalizability for Locomotion Mode Recognition in Prosthetic Gait
Bibliographic record
Abstract
The paper addresses the challenge of improving locomotion mode recognition (LMR) for lower limb prosthetic users (LLPU) by developing more generalizable machine learning (ML) models. Current models are limited to subject-specific models mostly as subject-independent models are hindered by the high variability within the LLPU population and the limited availability of LLPU data. The paper investigates leveraging able-bodied (AB) datasets to enhance model generalizability by first identifying more appropriate sensor locations. Different methods are tested that utilize the AB and LLPU datasets in different ways for feature selection and model training to optimize the performance of subject-independent ML models. It is shown that using vertical sensor combination on the intact side of LLPUs, feature selection with only LLPU and then training with both datasets combined, can greatly enhance LMR accuracy, achieving a 91.8% accuracy with a linear discriminant analysis (LDA) model. This approach aims to reduce the need for extensive training sessions for new users while maintaining high accuracy.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".