A Descriptive Analysis of Kinematic and Electromyographic Relationships of the Core during Forward Stepping in Beginning and Expert Dancers
Bibliographic record
Abstract
While electromyographic (EMG) and ki-nematic data in dance are accumulating, to date these data have raised more questions than they have answered. The purpose of this study was to introduce ensemble averaging into this body of literature as a way of dealing with the high levels of within-subject and between-subject vari-ability that have been previously reported. This study also introduces analysis during a forward weight shift, an analysis currently absent from the literature. Three collegiate novices (18.7 ± 0.6 years of age) and three expert dancers (27.7 ± 5.5 years of age) were studied in-depth. EMG data were collected continuously at 600 Hz for analysis of onset of activity for abdominal and erector spinae muscles. Kinematic data were collected continuously at 120 Hz from markers on the acromion and the greater trochanter for analysis of the verticality of the trunk. Data were collected continuously for over 4 seconds to include: baseline data prior to movement on a right legged balance, data for movement into plié fondu on the right leg, data for a forward step to the left leg, and baseline data at resolution on a left legged balance. For analysis, data were synchronized by time using onset of vertical ground reaction forces recorded by a force plate under the initial stance leg. All participants were tested on two separate days to assess day-to-day variability. Fifteen trials were collected on each day for each individual. Ensemble averaging of continuously recorded data was used to create line graphs for visual inspection, first to compare day-to-day congruence for each individual, next to assess within group variability, and finally to compare composite graphs between groups. Day-to-day variations for each individual were minimal. Differences were seen between members of the Beginner group but not the Expert group. Between group comparisons revealed the following differences: Experts appeared to use an anterior core support strategy while Beginners appeared to use a posterior core support strategy, Experts dis-played less EMG and kinematic variability than Beginners, and Experts maintained a more vertical posture throughout. Surprisingly, even though Experts were more verti-cal, they demonstrated the same amount of overall anterior-posterior sway as the Beginners. This finding leads to discussion of the dynamic nature of neuromuscular coordination patterns in maintenance of verticality. Issues surrounding the inability of statistically constructed models of human kinematic data to accurately represent individuals in groups are also discussed. Finally, applications of these findings to teaching and learning are offered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".