Characterizing sEMG Feature Errors in Lower Limb Muscles Due to Electrode Location and Skin–Electrode Interface Changes
Bibliographic record
Abstract
Surface electromyography (sEMG) is popular for monitoring muscle activation. However, it remains underutilized in clinical settings such as stroke rehabilitation. This is largely due to inter-session errors from variabilities in electrode location, the skin-electrode interface, and other inter-session effects. These errors obscure the ability to attribute sEMG signal changes to physiological patient changes. In this study, we quantified different sources of error by applying high-density sEMG (HDsEMG) arrays on the gastrocnemius medialis, tibialis anterior, semitendinosus, and tensor fascia latae of 12 healthy participants performing isometric and dynamic exercises common in stroke assessments. Between exercise sets (sessions), we shifted and reapplied HDsEMG arrays to analyze three error conditions: 1) same session errors (SSE) arising from changes in the recording electrode and electrode location, 2) same location errors (SLE) from changes in recording electrodes at the same location across sessions, and 3) same electrode errors (SEE) from comparing the same recording electrode at a different location across sessions. For SSE, frequency-domain features (mean, median, peak frequency) showed50% error at 10cm between electrodes. Contrastingly, we saw <19% error across frequency- and time-domain features for SLE and SEE after correcting for differences in electrode location using SSE trends. These results show differences in electrode location were most responsible for sEMG feature errors. Thus, accounting for electrode location could improve inter-session sEMG feature comparisons, enhancing the viability of sEMG-informed physiological assessments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".