Sensorization of Things Intelligent Technology for Sport Science to Develop an Athlete’s Physical Potential
Bibliographic record
Abstract
Sports can build both strength and be fun at the same time. When it comes to a professional athlete's victory, the winner will enhance his reputation and can earn a great deal of money. The problem that athletes frequently encounter is traumas, which can happen in games or during training. Today, the technology is very advanced and modernized. Innovations and perceptual devices can be created to monitor, measure, analyze and evaluate data from the information received from the introduction of an intelligent system of evaluation and prediction in terms of safety and risk prevention with regard to athletes, by collecting heart rate data, blood oxygen measurement, air density around the athletes’ body, body temperature and a temperature in the sports training room. This is the point of view and vision of a sports industry leaders, who perceive the opportunity and advantage associated with the competition and training of both amateur and professional athletes, to develop the athlete’s physical potential to achieve international excellence. The study of intelligent sensor technology that is used to support devices that work on the guidelines of Internet of Things (IoT) leading to the development of real time data collecting applications. These are used to process or interpret physiological knowledge for an analysis of results and for the prediction of incoming results. It was found that using intelligent sensing technology which are two main groups of detection and measurement: First, human body function sensing and second, measurement to environment around the body or object, and explained to detail in this article.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".