A Long Short-Term Memory-Based Interconnected Architecture for Classification of Grasp Types Using Surface-Electromyography Signals
Bibliographic record
Abstract
Reliable classification of grasp types from human limbs has become an important aspect used by applications with humanoid robotic systems, because of their high-accuracy implementations in human movement replication and detection. Biomedical features extracted from muscular signals are commonly used for this purpose, however, their extraction and usage have been targeted independently, with time series features not even considered in the classification stage. Recently, studies show deep neural networks could obtain the signal's features in their internal architecture and use them directly over a classification task, avoiding all preprocessing steps and improving the obtained accuracy. Therefore, the current study proposes a deep architecture based on long short-term memory networks for the classification of six grasp types as an end-to-end deep model approach, working with raw surface electromyography signals. Classification accuracy of 99.12% was obtained and compared with previous studies which use different machine learning techniques over the same dataset. Results obtained showed that our model's architecture improves previous results as well as provides a robust solution avoiding overfitting, with an F1-score higher than 99% for all grasp types.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".