A Classical Machine Learning Approach For Emg-Based Lower Limb Intention Detection For Human-Robot Interaction Systems
Bibliographic record
Abstract
Surface Electromyography (sEMG)-based intention-detection systems of lower limb can intelligently augment human- robot interaction (HRI) systems to detect subject’s walking direction prior-to or during walking. Ten Subject-Exclusive (Subj-Ex) and Generalized (Gen) Classical Machine Learning (C-ML)-based models are employed to detect direction intentions and evaluate inter-subject robustness in one knee/foot- gesture and three walking-related scenarios. In each, sEMG signals are collected from eight muscles of nine subjects during at least nine distinct gestures/activities. Linear Discriminant Analysis (LDA) and Random Forest (RF) classifiers, applied to the Time-Domain (TD) feature set (of the four input sets), provided the best accuracy. Subj-Ex approach achieves the highest prediction accuracy, facing occasional competition from the Gen approach. In knee/foot gesture scenario, LDA reaches an accuracy of 91.67%, signifying its applicability to robotic-assisted walking, prosthetics, and orthotics. The overall prediction accuracy among walking- related scenarios, though not as remarkably high as in the knee/foot gesture recognition scenario, can reach up to 75%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".