Hammer Throw: A Pilot Study for a Novel Digital-Route for Diagnosing and Improving Its Throw Quality
Bibliographic record
Abstract
The world record of the hammer throw has not been broken since 1986. This stagnation is multifactorial. One dominant factor could be the lack of evidence-based scientific/biofeedback training. This study aims to identify key parameters influencing throw quality and structure a new digital method for biofeedback training. Wire-tension measurement and 3D motion capture technology (VICON 12-camera system) were applied in quantifying and comparing throws of a national-level and a college-level athlete. Our results reveal that multi-joint coordination influences heavily on wire-tension generation. Four phases, i.e., initiation, transition, turns, and throw, play various roles in evaluating the quality of a throw. Among them, the transition, the third turn, and the throw display explosive/rapid increases of tension. For improving the effectiveness of the skill, the whip-like control and proper SSC (stretch-shortening cycle) of muscle groups involved should be established through years of training. Furthermore, our study unveils that quick and complex full-body control could be quantified and characterized by four key parameters: wire-tension, hand- and hip-height, and trunk tilt. Hence, a wearable digital device with tension and three Inertial Measurement Unit (IMU) sensors would have great potential in realizing real-time biomechanical feedback training in practice for evaluating and improving the efficiency of various training programs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".