Biosiglive: an Open-Source Python Package for Real-timeBiosignal Processing
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
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.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.001 |
| 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