Portable Electromyography: A Case Study on Ballistic Finger Movement Recognition
Bibliographic record
Abstract
In the neuromuscular analysis, electromyography (EMG) is typically used to analyze aggregate action potential (AP) signals to detect medical abnormalities, activation levels, and recruitment order, or analyze biomechanics. In our previous work, we compared the performance of these off-the-shelf solutions to research-grade EMG machines and found that due to their rigid electrode placement, low sampling rate, and data transmission medium, they are ill-suited for research use, in which data collection must be robust and accurate. We present XTREMIS: a low-cost and portable EMG platform with a small form factor (55 mm × 35 mm) that has a sample rate comparable to research-grade EMG machines. Indeed, the experiments on eight subjects have shown that not only does XTREMIS functionally outperform technologies, but also its signal quality is high enough to achieve finger movement classification accuracy similar to research-grade EMG machines, making it a suitable platform for research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".