A Quantitative Comparison of Slackline Balancing Capabilities of Experts and Beginners
Bibliographic record
Abstract
Mechanical stability criteria are able to explain balance and robustness during simple motions, however, humans have learned many complex balancing tasks for which science lacks a thorough understanding. In this work, we analyzed slackline balancing to define general balance performance indicators. The goal is to not only measure slackline expertise, but to be able to quantify stability during any balance task. For this, we compared beginners that had never balanced on a slackline before to professional slackline athletes. Further, all participants performed a static balance test, based on which we divided beginners into a balance-experienced and a balance-inexperienced group. On average, the balance experienced group was able to balance twice as long on the slackline and therefore, we showed that this static balance experience is a predictor of slackline balance performance. Based on over 300 balancing trials on the slackline of 20 participants, we then defined and evaluated over 30 balance metrics. The parameters can be grouped into quantification of stability and recovery movements, balance specific skills and balance strategies. We found that normalized angular momentum and center of mass acceleration are measures for overall stability, with lower values representing better stability and fewer recovery movements. We showed that improved hand coordination and adjusted stance leg compliance are valuable skills for balance tasks. especially when controlling external forces. Looking at posture and movement strategies, we found that professional slackliners have adapted a different mean pose with larger inertia and an upright head position, when compared to beginners.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".