A compact biomechanical feedback device for the training of hammer throwers
Bibliographic record
Abstract
In those elite sports involving fast movements and complex motor skills, like the hammer throw, the infield and real-time biomechanical feedback can be used to improve the training efficiency to facilitate athletic performance. In this paper, we have improved our previous design of a wearable and wireless sensor system for establishing the real-time biomechanical feedback training in the hammer throw as follows: (1) using two inertial measurement units (IMUs) and one load cell to obtain selected biomechanical parameters, and (2) designing a printed circuit board (PCB) to miniaturize the wearable device. The wearable system was developed based on Arduino open-source platform. The current wearable device's physical size was almost half of the previous one. The mean relative error of the calibration equation for a load cell embedded in the system was 0.87%. The wearable system has potential to be combined with artificial intelligence for estimating selected joint angles on upper and lower limbs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".