Investigating the Impact of Training Protocols on Myoelectric Pattern Recognition Control in Upper-Limb Amputees
Bibliographic record
Abstract
Myoelectric control schemes, pivotal in the control of prosthetic limbs, are often developed and evaluated in ideal laboratory conditions. However, these controlled environments may not fully represent the diverse challenges users face in real-world scenarios. The present study aims to tackle some of the existing research limitations by exploring the influence of various model training protocols on myoelectric pattern recognition within a semi-autonomous control system, which has been shown to reduce user cognitive load and enhance overall system performance. Specifically, we focus on the effects of limb movement and weight-bearing activities. We investigate the effect of four distinct training protocols in pattern recognition control for upper-limb prostheses, including training without a prosthetic hand, training with a prosthetic hand and static gestures, training with a prosthetic hand and dynamic movements guided by a graphical user interface (GUI), and training with a prosthetic hand having dynamic transfers and unguided. By examining these conditions, we aim to provide an understanding of how different training protocols and different labeling methods influence myoelectric pattern recognition control. Our results, based on experiments conducted with 14 non-disabled and one amputee participant, suggest that introducing the weight of the prosthetic hand and dynamic movements of the arm to the training data improves the accuracy and robustness of the control scheme. Real-time control experiments with a group of five non-disabled and one amputee participant using a multi-DOF prosthetic hand also verify our findings.
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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 | 0.004 |
| 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) | 0.001 | 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 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".