A dual-branch network architecture for sEMG-based gesture recognition
Bibliographic record
Abstract
The surface electromyography (sEMG) signal, as a type of bioelectrical signal, has been widely applied in modern human-computer interaction, especially for gesture recognition. The rapid advancement of deep learning has significantly promoted the development of sEMG-based gesture recognition technology. However, existing studies often face challenges such as insufficient feature extraction from sEMG signals and low differentiation between similar gestures. To address these issues, this study proposes a novel dual-branch model architecture specifically designed for sparse-channel sEMG gesture recognition. The model leverages the strengths of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory-Transformer (BiT) networks to process both the time-frequency representations and raw signals of sEMG data, thoroughly extracting spatiotemporal features. Additionally, the proposed Hybrid Attention Block (HAB) further enhances the feature representation capability of the CNN branch. To verify the model's effectiveness, multiple experiments were conducted on the NinaPro-DB1 dataset. The results demonstrate that the proposed model achieved a classification accuracy of 89.23%, outperforming most mainstream models.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".