An Exploratory Study of a Choreographic Approach to Golf Swing Dynamics: Bridging Biomechanics and Laban Movement Analysis
Bibliographic record
Abstract
This study introduces an innovative integration of Laban Movement Analysis (LMA) with biomechanical principles to examine the golf swing dynamics from an ecological perspective. Traditionally, LMA focuses on the qualitative aspects of movement, often isolated from external influences. This research bridges that gap by investigating how golfers manage and adapt to the inertial forces of the club throughout the swing. Using motion tracking sensors and screw theory, we analyzed the spatial movement pattern in the Kinesphere (mapped as an icosahedron) and related it to force dynamics in the Effort Cube through the inertia tensor. The results showed significant differences between skilled and novice golfers in terms of how efficiently they align their movements with the club's inertia. Skilled golfers demonstrated smoother Instantaneous Screw Axes (ISAs) and better synchronization with inertia forces, while novice golfers exhibited more abrupt deviations. These findings suggest that integrating qualitative movement descriptors with biomechanical models provides deeper insights into swing efficiency, performance improvement, and injury prevention. This combined framework offers a novel method to enhance both qualitative and quantitative analysis of golf swings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".