A 3-D-Printed Portable EMG Wristband for the Quantitative Detection of Finger Motion
Bibliographic record
Abstract
It is highly required to develop an affordable surface electromyography (EMG) wristband for the improved control of prostheses that is suitable to mimic human hand functions. By using the 3-D-printing technology, the wristband is customizable and feasible for various arm sizes or shapes. In this article, we have developed the 3-D-printed wristband for the control application. It consists of five pairs of custom-made serpentine dry electrodes, one EMG sensor, and one signal processing printed circuit board (PCB) to detect the user’s finger movements simultaneously. The wristband demonstrated its stability in EMG signal processing with a single-sensor system, which differed from other multisensor system devices. We verified the stability of the serpentine electrodes as bendable and stretchable. The serpentine electrodes have improved signal detection by providing conformal contact to the skin surface compared with the existing rigid dry electrodes. The flexibility allowed the electrodes to be placed in any shape of the skin surface. In a series of tests performed by the volunteer, we showed that the collected EMG signals reflected the muscle movements through signal processing. Under the muscle contraction of 75 lb, the wristband showed a signal sensitivity of 0.556 mV/lb with a 27-dB signal-to-noise ratio (SNR) for its valid EMG sensing capability. In addition, we also demonstrated the relationship between signal intensities and muscle forces at the different levels quantitatively. This work shows promising potential toward the advanced control system of prosthetic fields and its corresponding market.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".