Machine Learning-Based Intelligent Smart Embedded Sensors for Automatic Detection and Classification of Neuromuscular Disorders using EMG Signals
Bibliographic record
Abstract
Abstract The objective of this work is to create a novel computer-aided health monitoring system for diagnosing neuromuscular disorders (NMDs). Additionally, we will propose the use of embedded sensor networks to facilitate proactive patient care and remote health monitoring. The proposed method combines the discrete wavelet transform (DWT) with two supervised machine learning algorithms: the multi-class support vector machine (SVM) and the k-nearest neighbors (k-NN) classifiers. The dataset includes ten normal subjects, aged between 21 and 37 years. Out of these subjects, six are males and four are females. The results were presented on a graphical user interface (GUI) based on LabVIEW and implemented using a real embedded CompactRIO-9035 real-time controller. Additionally, the proposed embedded system has the capability to serve as a portable diagnostic device for the automatic detection of NMDs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".