ThrombUS+ D4.5: Testing, signal quality and performance enhancement of the wearable sensor network
Bibliographic record
Abstract
The aim of this document is to define testing protocols and procedures, as well as to describe the test resultsfor individual modules of the wearable sensor network (WSN), in order to ensure compliance with systemand user requirements. The document focuses on testing four components of the wearable sensors network(WSN): the Electrical Impedance Module (EIM), the Light Rheography Module (LRM), the Limb ActivityModule (LAM), and the textile wearable used for mounting all sensors.The hardware testing of electrical impedance module (EIM), light rheography mpodule (LRM) and limbactivity module (LAM), covering both the measurement performance and device’s general functionality,showed that all devices are sufficiently accurate, perform as expected and cover all user and systemrequirements defined in D2.3All test results are presented in protocol tables, which indicate which requirements are validated by specifictests. Where certain tests require more detailed explanation, this is provided below the corresponding tables.Additionally, the document includes demonstration sections for each module, aimed at showcasing thesystems and their user interfaces in operation.The functional testing of the textile wearable of the ThrombUS+ wearable sensor network showed that thedesign is suitable for integrating all systems developed in WP3 and WP4 effectively and ergonomically. Thecleaning procedure tests of wearable showed that the prototype sustained its mechanical and electricalproperties after the cleaning procedures.This document will be updated as necessary throughout the duration of the project, incorporating relevantinformation, issues, and procedural changes. Each time the document is revised, all partners will be dulyinformed of the updates and the changes made compared to the previous version.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".