EMGNet: A large-scale EMG dataset for locomotor intent recognition
Bibliographic record
Abstract
Surface electromyography (sEMG) has been used to control bionic legs through locomotor intent classification (i.e., determining locomotor tasks such as incline stairs or level ground). However, current EMG systems have typically been developed using small-scale data with limited individuals and historically have performance limitations between users and sessions due to differences in sensor positioning and/or user physiology. Here, we developed EMGNet to support the development of robust EMG-based intent recognition systems trained and evaluated on large-scale data. The dataset builds on many leading open-source datasets of locomotor tasks published to date – outlined in section 3 – by creating a large meta-dataset with EMG signals processed and prepared for developing intent recognition systems. Each dataset included has been modified to remove unneeded data (such as IMU or other sensors), achieve a consistent data structure for ease of use, and establish a standardized pipeline for preprocessing (filtering, normalization, windowing) and training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.028 |
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".