A Comparison of Bioelectric and Biomechanical EMG Normalization Techniques in Healthy Older and Young Adults during Walking Gait
Bibliographic record
Abstract
This study compares biomechanical and bioelectric electromyography (EMG) normalization techniques across disparate age cohorts during walking to assess the impact of normalization methods on the functional interpretation of EMG data. The biomechanical method involved scaling EMG to a target absolute torque (EMGTS) from a joint-specific task and the chosen bioelectric methods were peak and mean normalization taken from the EMG signal during gait, referred to as dynamic mean and dynamic peak normalization (EMGMean and EMGPeak). The effects of normalization on EMG amplitude, activation pattern, and inter-subject variability were compared between disparate cohorts, including OLD (76.6 yrs N = 12) and YOUNG (26.6 yrs N = 12), in five lower-limb muscles. EMGPeak normalization resulted in differences between YOUNG and OLD cohorts in Biceps Femoris (BF) and Medial Gastrocnemius (MG) that were not observed with EMGMean or EMGTS normalization. EMGPeak and EMGMean normalization also demonstrated interactions between age and the phase of gait in BF that were not seen with EMGTS. Correlations showed that activation patterns across the gait cycle were similar between all methods for both age groups and the coefficient of variation comparisons found that EMGTS produced the greatest inter-subject variability. We have shown that the normalization technique can influence the interpretation of findings when comparing disparate populations, highlighting the need to carefully interpret functional differences in EMG between disparate cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".