An sEMG-Controlled Prosthetic Hand Featuring a Tiny CNN-Transformer Model and Force Feedback
Bibliographic record
Abstract
This paper introduces the design of a wireless prosthetic hand with surface electromyography (sEMG)-based control and force feedback. The system features a novel CNN-Transformer model for classifying 21 gestures from sEMG signals. With a tiny size of only 169 kB, the proposed CNN-Transformer model achieves a high average accuracy of 81.39% when evaluated on a public dataset across 10 subjects. The system integrates wireless electronics into a 3D-printed open-source robotic hand. A custom-designed flexible sEMG armband is used for signal acquisition and stimulation delivery. A mobile device is used for real-time model inference. By employing hardware machine learning accelerators on the mobile device, a short inference time of 0.375 ms was achieved, which is 3.2x faster compared to using the CPU alone, rendering a smooth user experience with low latency. Force sensors were mounted on the robotic hand’s fingertips, and the outputs were modulated and delivered as electrical stimulation through the surface electrodes to provide force feedback to the user. The developed deep learning model and system design approaches have broad applicability across a wide range of prosthetic applications.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".