Clinician-accessible motor assessment with surface EMG: Key parameters and reliability
Bibliographic record
Abstract
Despite the evident value of surface electromyography (EMG) in neurorehabilitation, its clinical use is limited. Researchers have developed many sophisticated EMG methods to test scientific hypotheses and address technical issues. However, there is a lack of simple and easily reproduced (i.e., clinician-accessible) procedures with available reference literature to support interpretation. To make EMG assessments accessible and interpretable for clinicians, we propose a template for surface EMG acquisition and data analysis using stereotyped movements and manual cursor placements. We apply our template by creating a simple protocol for measuring the root mean square (RMS) and mean frequency (MNF) of the EMG signal in active muscles during hand opening, wrist extension and flexion, and elbow flexion. In 36 healthy males and females, we assess interclass correlations (ICCs) to evaluate the relative inter-rater reliability of manual cursor placements, and the relative inter-session reliability of the MNF and RMS values. We also assess smallest detectible change (SDC) between assessments as a function of the number of contributing measurements (i.e., repetitions). Manual cursor placement exhibited excellent inter-rater reliability (ICC > 0.9) and inter-session reliability of MNF and RMS feature measurements was good (0.75 > ICC > 0.9) or excellent. As expected, SDCs decreased as movement repetitions increased. Compared to a single RMS measurement, taking the 14-repetition mean lowered SDC 95 by 21% for elbow flexion, 118% for wrist extension, 66% for wrist flexion and 15% for hand opening. Compared to a single MNF value taking a 14-reepetion mean reduces the SDC 95 by 3 Hz for elbow flextion, 4 Hz for wrist extension, 5 Hz for wrist flexion, and 4 Hz for hand opening. We demonstrate a clinician-accessible template for reliable EMG assessment, and an intuitive approach to interpreting changes in the obtained measurements. Designing protocols explicitly for broad use by clinicians will be necessary to advance clinical acceptance and integration of the modality into practice.
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".