A 3D-Printed, Adjustable Armband for Electromyography-Based Finger Movement Classification With Haptic Feedback
Bibliographic record
Abstract
Recent work in prosthetic devices suggests that forearm surface electromyography (sEMG) is a promising technology for human-computer interactions. Specifically, a system able to detect individual finger movement can have many clinical and nonclinical applications. Popular consumer-grade sEMG armbands are limited by their fixed electrode arrangement, which can negatively affect the classification of subtle finger gestures. We propose a low-cost, 3D-printed armband with fully adjustable electrode placement for the detection of single-finger tapping motions. We trained machine learning classifiers on features extracted from eight-channel sEMG signals to detect movement from nine fingers. We obtained a classification accuracy of 71.5 ± 1.1% for a K-Nearest Neighbours (KNN) classifier using features extracted from 500 ms windows of sEMG data. Moreover, a KNN model trained on 200 ms windows from a subset of particularly clean data obtained an accuracy of 93.0 ± 0.5%. We also introduce a novel haptic feedback mechanism to improve user experience when using the armband, and propose an augmented reality typing interface as a potential application of our armband.
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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) | 0.000 | 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".