Efficient correction of armband rotation for myoelectric-based gesture control interface
Bibliographic record
Abstract
OBJECTIVE: The appearance of commercial myoelectric armbands has greatly increased the portability and convenience of myoelectric controlled interfaces (MCIs). However, one limitation of the current state-of-the-art myoelectric control algorithms is that they have poor robustness against armband displacements, especially rotation, leading to great algorithmic performance degradation. The traditional remedy, retraining the interface, requires the data collection of all gestures and is impractical in many applications. The recently proposed position verification (PV) framework focused on quickly identifying and correcting the electrode positions after the displacement, showing the potential to restore the performance of MCI in a faster way. However, its online effectiveness is still yet to be validated. APPROACH: This work proposed a novel algorithm of identifying the rotation direction to improve the efficiency of the PV framework and demonstrated the real-time capability of the PV framework using a commercially available armband. MAIN RESULTS: The results showed that with PV, a 1.5-cm rotation could be corrected with an average of 3.1 ± 1.5 interactive adjustments, equivalent to around 15.5 ± 7.5 s, which was greatly reduced compared to retraining. There was no significant difference in the real-time control performance between before the armband displacement and after the PV correction. SIGNIFICANCE: To the best of our knowledge, this study was the first maintaining pattern recognition-based myoelectric control performance in the presence of electrode shifts without recollecting the entire training data. It suggested the feasibility of the PV framework used in the myoelectric armband and MCI for practical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".