A wearable EMG sensor for continuous wrist neuromuscular activity for monitoring
Bibliographic record
Abstract
Carpal Tunnel Syndrome (CTS) is an entrapment neuropathy that affects 3-6% of the adult population globally. About 90% of nerve-damaging diseases are labeled CTS when diagnosed, making it one of the most prominent nerve dysfunctions in the current population metrics. CTS cases have soared in a world where digital gadgets are progressively gaining traction. There is also a high onset of CTS in laborers specializing in fine motor skills. A patient with CTS typically entails symptoms of pain, numbness, and tingling in the wrist. However, accurate diagnosis and consistent checkups for CTS are becoming a bigger issue. In this research paper, we demonstrate the utilization of various diagnostic tests in supporting CTS diagnosis and the necessity for a monitoring system to track wrist neuromuscular activity. By analyzing traditional assessment techniques and identifying their limitations, we can enhance diagnosis, making it easier for patients to deal with the condition. This approach has culminated in the development of our prototype, BracEMG. Through a wearable EMG sensor, our prototype aims to track possible signs of CTS or other nerve dysfunctions at the wrist. The findings gained through an EMG test support our research by outlining the prevalence of CTS across different age groups and illustrating the levels of muscular activity in a graphical format. Our results demonstrate overall muscle activity at the wrist. Since no universally accepted standard for diagnosing CTS exists, we envision our work as the starting point for more extensive research. The limitations of our prototype can become the foundation for future developments in the industry revolving around CTS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".