ML-Based Identification of Neuromuscular Disorder Using EMG Signals for Emotional Health Application
Why this work is in the frame
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Bibliographic record
Abstract
Abstract: The electromyogram (EMG), also known as an EMG, is used to assess nerve impulses in motor nerves, sensory nerves, and muscles. EMS is a versatile tool used in various biomedical applications. It is commonly employed to determine physical health, but it also finds utility in evaluating emotional well-being, such as through facial electromyography. Classification of EMG signals has attracted the interest of scientists since it is crucial for identifying neuromuscular disorders (NMDs). Recent advances in the miniaturization of biomedical sensors enable the development of medical monitoring systems. This paper presents a portable and scalable architecture for machine learning modules designed for medical diagnostics. In particular, we provide a hybrid classification model for NMDs. The proposed method combines two supervised machine learning classifiers with the discrete wavelet transform (DWT). During the online testing phase, the class label of an EMG signal is predicted using the classifiers’ optimal models, which can be identified at this stage. The simulation results demonstrate that both classifiers have an accuracy of over 98%. Finally, the proposed method was implemented using an embedded CompactRIO-9035 real-time controller.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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