Bibliographic record
Abstract
IntroductionFor researchers, surface electromyography (EMG) seems a seductive technology because it provides a non-invasive means to acquire in-vivo data about physiological processes that cause muscles to produce movement.It captures muscle activity during any movement of a body by measuring seizure potentials released by the muscles when they are activated.Consequently, measured potentials are typically used to fractionate a body's response process as well as get timing of neural control and coordination among muscle groups.Such analyses permit assessments of technical skills which, in turn, provide foundational information for biofeedback training and development of new pedagogical methods.Consequently, in high-skill activities such as those of the performing arts, EMG would appear to have significant relevance in terms of skill optimization and learning.On the surface, it is apparent that arts performance shares many characteristics, including health risks, with an area where EMG research has already had a significant disciplinary impact -sports (Chesky, et al., 2002; Lehmann & Davidson, 2002; Wilson, 1986).Pedagogy and training in both sports and performance arts aim to develop physical skills efficiently while avoiding injury for participants.While EMG applications in human sports movements have been an acknowledged aid in improving sport performance for decades (e.g. the Journal of Electromyography and Kinesiology has a 21-year history in this regard), applications in arts performances are still rare.Further, a review of the literature shows that, EMG applications in the arts tend to fall into only three broad categories: skill analysis descriptions and establishment of norms, training method evaluations, biofeedback in both pedagogy and injury remediation (Kjelland, 2000).This book chapter details selected examples where EMG technology is used in applications pertinent to arts performance.Since almost all EMG studies rely upon initial skill analysis and description in order to establish norms, research in the performance arts is often a multi-stage process.Exemplars for purposes of discussion will be drawn from instrumental and vocal performance, as well as ballet.The goals of the selected research focus on: 1) describing the activity of selected muscles, 2) identifying innate abilities or talents, 3) quantifying the efficiency and effectiveness of pedagogical methods, 4) diagnosing technical difficulties, or 5) determining loading intensity during performance and evaluating factors related to pain and injury.Given the small body of existing EMG studies in the arts, the examples below are mainly chosen to elaborate the potential and relevance of EMG research in the performing arts as well as to encourage more research in the area. www.intechopen.com How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: Gongbing Shan and Peter Visentin
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".