Obtaining Vital Distances Using Wearable Inertial Measurement Unit for Real-Time, Biomechanical Feedback Training in Hammer-Throw
Bibliographic record
Abstract
The hammer throw is one of the regular track and field competitions, but unlike other events, it has not seen a new world record for over three decades. The standstill may be caused by the lack of scientifically based training. In our previous work, we have developed a wireless/wearable device for the wire tension measurement in order to develop real-time biomechanical feedback training. In this paper, we show the improvement of our wearable system by adding two sensors for tracking of two vital vertical distances. The paper describes the details related to the development of turning an inertial measurement unit into a tracking device for the dynamic distances. Our preliminary data has shown that the dynamic data of the hip and wrist could be used for revealing the coordination between the upper and the lower limbs during a throw. In conjunction with wearable wire-tension measurement, various motor control patterns employed for hammer throwing could be demystified. Such real-time information could be valuable for hammer-throw learning and optimization. Further studies are required to verify the potentials of the wearable system for its efficiency and effectiveness in coaching practice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".