Measuring Acceleration of a Trampoline Circus Act during Training and In-Show Using Wearable Technology
Bibliographic record
Abstract
Circus performance acts incorporate a variety of unique movements. Professional artists are often performing these acts in multiple shows a day and/or week on top of completing regular training of the acts. However, there is limited understanding of the mechanical demands of the acts. The emergence of wearable technology has allowed for measuring some mechanical aspects of these highly complicated acts. PURPOSE: Compare acceleration profiles of a trampoline circus act recorded during training and in-show. METHODS: Seven acrobats (1.75±0.05m; 78.83±4.49kg; 28.93±3.3 years) performed 3 training acts on separate days and 2 show acts on one day. Following the completion of the show, participants reported their rating of perceived exertion (1-10 scale). Tri-axial accelerations were measured using a commercial accelerometer system (Hexoskin, Carre Technologies Inc, Montreal, CA) with the accelerometer located on the lateral aspect of the right hip. Average resultant acceleration (AVG) was calculated each trial during training (3 trials) and show (2 trials). Time based acceleration data were also classified into 5 ranges: 0 ≤ Very Low < 0.1g; 0.1 ≤ Low < 0.3g; 0.3 ≤ Moderate < 0.6g; 0.6 ≤ High < 1; 1 ≤ Very High ≤ 16g. Relative time spent in each range was averaged during training and in-show. Dependent variables were compared using paired-sample t-tests and a repeated-measures ANOVA. RESULTS: AVG was significantly higher during training vs. in-show (0.525±0.12g vs. 0.467±0.098g; p=0.030, effect size= 0.53). RPE was significantly lower during training vs. in-show (2.757±0.53 vs. 3.429±1.10; p=0.027). RM ANOVA revealed no interaction between acceleration ranges and environment (p>0.05), and no main effect for environment (p>0.05). Time spent in acceleration ranges was influenced by level (p<0.05) such that there was a both a linear and cubic trend across bin levels. CONCLUSION: The lower AVG may reflect that the show environment promotes less intense movements to maintain synchronization with co-artists. Wearable technology may be useful for analyzing show movements in a way to better develop effective training programs. Supported by Cirque Du Soleil
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".