A Wireless Sensor System for the Training of Hammer Throwers
Bibliographic record
Abstract
Hammer throw has a long-standing history in track and field, but unlike other events, hammer throw has not seen a new world record since 1986. One reason for this stagnation is the lack of scientifically based training. In this paper, we propose to establish scientifically described training targets and routes, which in turn require tools that can measure and quantify characteristics of effective hammer-throw. Towards this end, we have developed a real-time biomechanical feedback device -- a wireless sensor system -- to help the training of hammer throwers. The system includes two sensors -- an infrared proximity sensor for tracing the hip vertical movement and a load cell for recording the wire tension during a hammer throw. The system uses XBees for data transmission and an Arduino processor for the wireless system control. It is hypothesized that wire tension and vertical hip displacement measurements would be sufficient to supply key features when analyzing hammer throw.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".