EMGNet: An EMG Dataset for Locomotor Intent Recognition
Bibliographic record
Abstract
Surface electromyography (EMG) can be used to interact with and control robotic systems via intent recognition. However, most machine learning algorithms for EMG intent recognition have been trained using small-scale data with limited individuals, which can affect generalization across users and tasks. Motivated by these limitations, we developed a large-scale EMG dataset to support research and development in intent recognition systems, with an emphasis on human locomotion. Our new dataset combines multiple open-source datasets, as outlined in section 3, with processed EMG signals for healthy subjects. Each dataset that we included in our meta-dataset has been modified to achieve a consistent data structure for ease of use and to establish a standardized pipeline for data preprocessing (e.g., filtering, normalization, and windowing) and for training machine learning algorithms. Our EMG dataset is 152 GB comprised of 7 open-source datasets with 132 users total from four different countries. Signals include tibialis anterior, medial gastrocnemius, rectus femoris, and biceps femoris, and six activity classes, including standing, level-ground walking, stair ascent, stair descent, ramp ascent, and ramp descent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.019 |
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".