Data‐Driven Synchronization Analysis of a Bouncing Crowd
Bibliographic record
Abstract
Vibration serviceability problems concerning lightweight, flexible long‐span floors and cantilever structures such as grandstands generally arise from crowd‐induced loading, in particular due to bouncing or jumping activities. Predicting the dynamic responses of these structures induced by bouncing and jumping crowds has therefore become a critical aspect of vibration serviceability design. Although accurate models describing the load induced by a single person are available, essential information on the level of synchronization within the crowd is missing. In answer to this lack of information, this paper experimentally investigates the inter‐ and intraperson variability as well as the global crowd behavior in bouncing crowds. A group size of 48 persons is considered in the experiment whereby the individual body motions are registered synchronously by means of a 3D motion capture system. Preliminary tests verified a new approach to characterize the bouncing motion via markers on the clavicle. Subsequently, the full‐scale experimental study considered various crowd spacing parameters, auditory stimuli, and bouncing frequencies. Moreover, special test cases were performed whereby each participant was wearing an eyepatch to exclude visual effects. Through the analysis of 330 test cases, the interperson variability at the bouncing frequency is identified. In addition, the cross‐correlation and coherence between participants are analyzed. The coherence coefficients between each pair of participants in the same row or column are calculated and can be described by a lognormal distribution function. The influence of the spatial configurations and visual and auditory stimuli is analyzed. For the considered spatial configurations, no relevant impact on the inter‐ and intraperson variability in the bouncing motion nor in the global crowd behavior is observed. Visual stimuli are found to enhance the coordination and synchronization. Without eyesight, the participants are feeling uncertain about their bouncing behavior. The results evaluating the auditory cues indicate that significantly higher levels of synchronization and a lower degree of the intraperson variability are attained when a metronome cue is used in comparison to songs where the tempo often varies.
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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) | 0.000 | 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 | 0.001 |
| 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".