Classification of Muscle Inertial Motion and Electromyographic Activity Integration to Improve Accuracy in Pattern Recognition
Bibliographic record
Abstract
ABSTRACT Introduction Over the years, several studies have been published reporting the use of distinct sources of information used for pattern recognition that can be translated into commands to control human-machine interface system, for example, electromyography (EMG), pressure sensors, and accelerometers. Studies using muscle motion patterns and its combination with EMG in the context of pattern recognition for evaluation of the muscles and human-machine interface system in able-bodied individuals and limb-absent subjects are scarce. Material and Methods In this context, this research presents the assessment of the classification of patterns formed by features extracted from both muscle motion and electromyographic signals. Data sets were collected from both arms of five unilateral transradial limb-absent subjects and seven able-bodied subjects in the control group. The features from the EMG and the muscle motion such as amplitude, frequency, predictability, and variability of the signals were estimated. Results The results were presented in terms of the sensitivity, specificity, precision, and accuracy of the classifier. The combination of both measurements, EMG and muscle motion, defined the six basic movements for limb-absent subjects within an accuracy of 98% ± 1% for the sound forearm against 96% ± 4% for the amputated forearm. Conclusions For future work, it is expected that the strategy of classification and the combination of inertial and electromyographic activity will be used in actual scenarios for the controlling of artificial limbs and other applications related to human-machine interaction. Clinical Relevance The use of inertial sensors may increase the usability and accuracy of systems used for diagnosing, training, therapy, or controlling devices such as orthoses and prostheses.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".