IoT device for Athlete's movements recognition using inertial measurement unit (IMU)
Bibliographic record
Abstract
In modern sports training, collection, acquisition, and analysis of data concerning the athlete's activities are a necessity to improve trainings. This paper presents a new wearable IoT device, capable of monitoring athletes' movements in real time. The collected data are of a great importance for training sessions and improvement of athlete's performances. The proposed wearable device located on the athletes' wrist is an inertial sensing module that connects to Wi-Fi and sends information about Athlete activities. This paper presents also an electrical design proposed for such connected device using an inertial measurement unit (IMU) as a sensor. Furthermore, this article investigates different IoT communication technologies and protocol used for securing data transmission. An application was developed for data visualization and for activities classification. The application allows to remotely view the athletes' movement corresponding signals and detect the associated activity. The experimental results of this research show that the developed system is able to identify several activities of players during their training with a high success rate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".