Assessment of EMG Benchmark Data for Gesture Recognition Using the NinaPro Database
Bibliographic record
Abstract
In recent years, many electromyography (EMG) benchmark databases have been made publicly available to the myoelectric control research community. Many small laboratories that lack the instrumentation, access, and experience needed to collect quality EMG data have used these benchmark datasets to explore and propose new signal processing and pattern recognition algorithms. It is widely accepted that noise contamination can affect the performance of myoelectric control systems, and so useful datasets should maintain good signal quality to ensure accurate results for proposed EMG-based gesture recognition systems. Despite the availability and adoption of benchmarks datasets, however, the quality of the EMG signals in these benchmarks has not yet been examined. In this study, the signal quality of the Non-Invasive Adaptive Prosthetics (NinaPro) dataset, the most widely known publicly available benchmark database to date, was comprehensively investigated with the goals of: 1) reporting the level of noise contamination in each NinaPro sub-dataset, 2) proposing signal quality criteria for assessing EMG datasets, 3) analyzing the effect of signal quality on classification performance, and 4) examining the quality of the data labels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".