Biosiglive: an Open-Source Python Package for Real-timeBiosignal Processing
Bibliographic record
Abstract
biosiglive aims to provide a simple and efficient way to access and process biomechanical data in real time.It was conceived as user-friendly software aimed for both non-expert and expert programmers.The library uses interfaces to access data from several sources, such as motion capture software or any Python software development kit (SDK).Some interfaces are already implemented for Vicon Nexus motion capture (Oxford, UK) and Delsys electromyography SDK (EMG) (Boston, USA).That say, any additional interface can be added as custom interface using the abstract class.biosiglive was designed for biosignals, therefore, existing classes represent data collected from standard acquisition systems in biomechanics, such as markers for motion capture or EMG.Methods are available to process in real-time any input signal.Data can be saved in a binary file at each time frame to avoid any data loss in case of system shutdown.Data can also be displayed using the LivePlot class, which is based on PyQtGraph (C++ core) and allows, therefore, fast real-time displaying.Finally, 'biosiglive' was conceived as a flexible real-time data processing and streaming tool adaptable to various set-ups, software, and systems.Therefore, a TCP/IP connection module was implemented to send data to a distant port to be used by any other system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.027 |
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".