Convolutional Feature Engineering for Cross-day Personal Identification using Wrist Electromyography
Bibliographic record
Abstract
Surface electromyography (EMG) has emerged as a promising biometric modality for person identification. However, its performance can deteriorate over multi-day scenarios, necessitating robust feature extraction methods. With recent advancements in artificial intelligence, deep feature extraction and classification techniques have gained momentum in the biosignal domain. In this study, we propose MyoBM-Net, a convolutional feature extraction method for identification applications using EMG signals. Instead of using the conventional frequency domain-based feature extraction, 1D convolution and 2D convolution layers are utilized to extract spatial and channel-specific information, respectively. The performance evaluation utilized wrist EMG data from 43 participants on three different days across one month while performing hand/wrist gestures. A cross-day analysis, with training and testing data collected on separate days, was conducted to assess the robustness of EMG-based biometrics in practical settings. In cross-day identification, MyoBM-Net achieved a median rank-1 accuracy of 98.5% and outperformed the conventional feature extraction method. The proposed method resulted in a lower DBI value of 1.73, highlighting its strong potential for use in personal identification applications. The source code and results are available at https://github.com/pradhanashirbad/MyoBM-Net.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".