Somatosensory Electrical Stimulator for Assessment of Current Perception Threshold at Different Frequencies
Bibliographic record
Abstract
We aim to introduce the EELS device, an advantageous somatosensory electrical stimulator composed by hardware, firmware, and software to perform peripheral afferent fibers assessment based on sinusoidal current. We designed the EELS combining the precision given by STM32 microcontroller and the stability generated by the current source based on a bootstrap topology, a simplified and stable system if compared to a first equipment version. We coded the software as an Android Mobile Application (App) to have compatibility with mobile devices and reduce hardware set up time. Workbench tests shows EELS system operation capabilities in terms of Total Harmonic Distortion (THD), stimulus linearity, stimuli's frequency spectrum, and maximum current amplitude. The tests' results show an reduction in linearity when compared to the previous device, but the second order coefficient remains 10,000 times less than the first order coefficient. The bootstrap topology allows for a higher stimuli bandwidth up to 10,000 Hz, and the a higher current intensity (11.2 mA at maximum). Additionally, the App was stable during all tests and considered by us as intuitive and user friendly. Considering all improvements, EELS could outperform its predecessor, presenting a more intuitive and simple operation to break new grounds on research and clinical applications.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".