sEMG-Based Lower Limb Intention Detection using Artificial Intelligence and its Impact on Assistive Human-Robot Interaction
Bibliographic record
Abstract
People with mobility-related disabilities, including individuals with Cerebral Palsy (CP), can observe improvements in their quality of life by incorporating various types of robotic devices, e.g., rehabilitation, assistive, and Human-Computer Interaction (HCI) devices.To be compliant to users' needs, such robots should be intelligent in regards to their human partners' intention to be able to adapt themselves to their needs and provide beneficial feedback in executing a desired motion.For this purpose, a Surface Electromyography (sEMG)-based intelligent lower limb intention-detection system is studied to augment Human-Robot Interaction (HRI) by detecting subjects' walking direction prior-to or during walking.Ten Classical Machine Learning (C-ML)-based models with two different implementation strategies Deep Learning DL Ensemble Random Forest RF Deep Neural Network DNN Root Mean Square RMS Degree of Freedom DoF Root Mean Square Error RMSE
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".