IoT web-based platform for Athlete’s development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper aims to present a web-based platform development based on the integration of IoT functionalities. This developed platform is built for athlete’s workout routines to facilitate their basic training by collecting data during exercises and to improve their abilities during training sessions. The proposed approach allows coaches to monitor training progress in real-time as well as adjust routines and instructions during the sessions. This present work includes methods of sending, processing, storing and then accessing to the data collected by IoT devices during training, in order to visualize statistics and information on player’s performance. A customized cloudbased platform capable of analyzing, as an example, ball shots during a handball training was also presented. In addition, this platform will provide coaches with real-time access to player’s data and allow monitoring athlete’s exercises and managing specific trainings to improve performance results. A comparison of processing time is made for different commercial Cloud platforms widely used such as Google Cloud, Microsoft Azure and Amazon web Services. Furthermore, this study presents an easyto-build cloud platform able to analyze athlete’s data collected using IoT devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it