Robust upper limb motion classification using Gaussian mixture models
Bibliographic record
Abstract
A Gaussian mixture model (GMM) based classification scheme is proposed in this paper to perform multiple limb motion discrimination using continuous myoelectric signals (MES) from limb muscles. The system is optimized with respect to the feature set, classifier and post-end processing of the decisions through comprehensive experimentation. The experiments examine the effects of various feature sets including the time-domain (TD) features and the autoregressive (AR) features with root mean square value (RMS), and the effect of the majority vote (MV) in post-processing on the classification performance. The averaged GMM classification performance is compared with that of three other motion techniques (a linear discriminant analysis (LDA), a linear perceptron (LP) neural network and a multilayer perceptron (MLP) neural network). The Gaussian mixture motion model achieves 96.91% classification accuracy using a combination of AR with RMS and TD (AR+RMS+TD) feature set for a six class problem. It has been demonstrated that this GMMbased limb motion classification scheme has superior classification accuracy and results in a robust method of motion classification.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".