Validity and Reliability of Surface Electromyography Features in Lower Extremity Muscle Contraction in Healthy and Spinal Cord–Injured Participants
Bibliographic record
Abstract
Background: Spinal cord injury (SCI) has a significant impact on motor control and active force generation. Quantifying muscle activation following SCI may help indicate the degree of motor impairment and predict the efficacy of rehabilitative interventions. In healthy persons, muscle activation is typically quantified by electromyographic (EMG) signal amplitude measures. However, in SCI, these measures may not reflect voluntary effort, and therefore other nonamplitude-based features should be considered. Objectives: The purpose of this study was to assess the correlation of time-domain EMG features with the exerted joint torque (validity) and their test-retest repeatability (reliability), which may contribute to characterizing muscle activation following SCI. Methods: Surface EMG (SEMG) and torque were measured while nine uninjured participants and four participants with SCI performed isometric contractions of tibialis anterior (TA) and soleus (SOL). Data collection was repeated at a subsequent session for comparison across days. Validity and test-retest reliability of features were assessed by Spearman and intraclass correlation (ICC) of linear regression coefficients. Results: In healthy participants, SEMG features correlated well with torque (TA: ρ > 0.92; SOL: ρ > 0.94) and showed high reliability (ICC mean = 0.90; range, 0.72–0.99). In an SCI case series, SEMG features also correlated well with torque (TA: ρ > 0.86; SOL: ρ > 0.86), and time-domain features appeared no less repeatable than amplitude-based measures. Conclusion: Time-domain SEMG features are valid and reliable measures of lower extremity muscle activity in healthy participants and may be valid measures of sublesional muscle activity following SCI. These features could be used to gauge motor impairment and progression of rehabilitative interventions or in controlling assistive technologies.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".