Design of a novel Simulink model for surface electromyographic (SEMG) sensor design for prosthesis control
Bibliographic record
Abstract
This paper presents design, simulation and fabrication of a surface electromyographic (SEMG) sensor for control of prosthetic devices. EMG activity is mainly the generation of a bio-potential signal (electrical signals) due to muscle action. These signals picked from motor points of muscles are contaminated with various intrinsic/extrinsic noises, which must be removed through different filtering techniques in order to develop a sensor that has a high signal-to-noise ratio. Power spectral density (PSD) of any EMG signal plays a vital role to determine the signal strength. A novel Simulink model has been developed which mimics various elements of an active SEMG sensor. By using this model, low/high/notch filters are designed and optimized. The model is also used to simulate the effects of these filters on power spectral density (PSD) of the EMG signal. Simulation of double rectification and smoothing (envelopment) is also carried out. EMG signals recorded from the Tibialis anterior, monopolar needle, and fine wire isometric contraction were used in this simulation. Finally, on the basis of simulation results, instrumentation of surface EMG sensor is designed and fabricated. Performance/results of developed SEMG sensor are in accordance with the simulation results of the developed Simulink model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".