Design and Analysis of Electronic Head Protector for Taekwondo Sports
Bibliographic record
Abstract
Electronic point scoring systems (PSS) for vests are heavily relied upon in taekwondo. However, no classification and assessment of legal and illegal taekwondo techniques exist. This is also referred to as hit-validation and the objective of this research is to create an electronic helmet (eHelmet)forhit-validation. Threemainstudieswereperformedtoachievethisobjective: Robustness Testing, Sensor Placement and Classification of Impacts to the head. The first two studies are preliminary to the main Classification of Impacts study. This is needed as no data sets using an IMU are currently available for taekwondo. Robustness Testing: proved that IMU can in-fact be used in the inherently harsh environments of taekwondo with a linear response. The calculated response for theIMUis: f(x)=mx+b,wheremis0.2947andbis1.499(accelerometer)andf(x)=mx+b,wherem is 28.33 and b is 84.8 (gyroscope). Sensor Placement: Qualitatively and quantitatively concluded the ideal location for the sensor and electronics is indeed the back of the head, based on durability, cost, human factors, and signal quality. Classification of Impacts: IMU classified real-world impacts with 90% accuracy. The two classes were roundhouse kick (legal) and punch (illegal). An eHelmet using an IMU is capable of classifying impacts with high accuracy. The benefit of our system includes low cost, lightweight algorithm for on-device computing (edge computing), and real-time classification. Furthermore, it possesses all the safety requirements of current protective headgear.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".