Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?
Bibliographic record
Abstract
Despite a historical focus on prosthetics, the incorporation of electromyography (EMG) sensors into less obtrusive wearable designs has recently gained attention as a potential human–computer interaction scheme for general consumer use. Because consumers are more used to wrist-worn devices, this article presents a comprehensive and systematic investigation of the feasibility of hand gesture recognition using EMG signals recorded at the wrist. A direct comparison of signal and information quality is conducted between concurrently recorded wrist and forearm signals. Both signals were collected simultaneously from 21 subjects while they performed a selection of 17 different single-finger gestures, multifinger gestures, and wrist gestures. Wrist EMG signals yielded consistently higher ($p< 0.05$) signal quality metrics than forearm signals for gestures that involved fine finger movements, while maintaining comparable quality for wrist gestures. Similarly, the performance of both individual state-of-the-art EMG features and a standard feature set was found to be significantly better when using wrist signals for single and multifinger gestures, and comparable for wrist gestures. Classifiers trained and tested using wrist EMG signals achieved average accuracy levels of 92.1% for single-finger gestures, 91.2% for multifinger gestures, and 94.7% for the conventional wrist gestures. In conclusion, this article clearly demonstrates the feasibility of using wrist EMG signals for hand gesture recognition. Results highlight not only the promise of this approach, but also the viability of incorporating prior knowledge from the prosthetics field in the design of wrist-based EMG pattern recognition systems.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".