Demographic-Driven Electromyography Analysis: Advancing Personalized Biosignal Interpretation
Bibliographic record
Abstract
The integration of rehabilitation robotics and surface electromyography (sEMG) offers a powerful approach for monitoring and enhancing recovery in patients with neuromuscular disorders. However, variability in baseline sEMG readings across individuals can limit its effectiveness. Factors such as age, height, and weight influence these baselines, and there is a lack of personalized baselines that account for demographic differences. This study proposes a novel model to estimate individualized baselines for one important sEMG parameter, Root Mean Square (RMS). Demographics and physiological data were collected from 30 healthy participants, and sEMG signals were recorded using four electrodes on the forearm muscles during a pushing task at two wrist positions. A Decision Tree Regression model was developed for each combination of the two wrist postures and four electrode locations, resulting in eight combinations, with optimal features identified using the Recursive Feature Elimination method. The regression models achieved accuracies ranging from 88.81% to 95.6%. A global sensitivity analysis using the Sobol method evaluated the importance of each input feature. Results indicate that gathering more comprehensive sEMG data for the most influential factors could improve model generalizability. The findings of this study offer a promising approach for individualized sEMG baselines, with potential applications in rehabilitation robotics to enable personalized recovery strategies for neuromuscular disorders.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".