Usporedba konvencionalne površinske elektormiografije i komercijalno dostupne mišićne narukvice za površinsku elektromiografiju s osvrtom na moguću kliničku primjenu
Bibliographic record
Abstract
Muscles and nerves of the human body represent a source of electric energy. Changes in the electric current inside the skeletal muscle can be detected using electromyography (EMG). EMG can be differentiated into needle EMG and surface electromyography (SEMG). Needle EMG and SEMG are both diagnostic methods. They help the clinician to determine whether a patient is being affected by a myopathic or a neuropathic disorder. In this paper, the physiology of the muscular contraction process is firstly explained in detail. Later on, the process of how the EMG sensors register and convert the electric current produced within the muscle into a graphic report are explained as well. Furthermore, this paper describes both the advantages and disadvantages of EMG and SEMG. Also, the indications and the properties of these diagnostic methods in a clinical setting are described. This paper also describes the current guidelines for the correct placement of SEMG electrodes on the skin. In addition, several new potential clinical applications for SEMG are described. A commercially available SEMG, the MyoArmband SEMG sensor (Thalmic Labs Inc., Kitchener, Ontario, Canada), is introduced in this paper and represented with its' advantages and disadvantages. Publications concerning the MyoArmband SEMG sensor which support the aforementioned advantages are further discussed. Later, potential clinical applications which tend to prove the therapeutic and diagnostic uses of the MyoArmband SEMG sensor are presented. Among these, biofeedback therapy together with qualitative measurement of muscle fatigue and contraction emergence latency measurement seem to have the best perspective. Altogether, this device, as well as SEMG procedure in general, hold great perspective for clinical use. Due to the non-invasive nature of the device application, paediatric patients would merely benefit from such biofeedback therapy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".