Improving Performance of Pattern Recognition-Based Myoelectric Control Using a Desktop Robotic Arm Training Tool
Bibliographic record
Abstract
Performance using myoelectric prostheses, especially considering recent developments in pattern recognition-based control, is significantly impacted by user training with the selected control strategy. However, minimal research has been done into the effect of functional user training with different myoelectric control strategies, since doing so typically requires training and evaluating prosthesis users with differing device configurations and customized socket fittings. Intermediate platforms such as desktop-mounted robotic arms present an opportunity for consistent training of participants both able-bodied and with amputations. In this paper, a training environment and protocol for improving myoelectric prosthetic control with a desktop-mounted robotic arm was developed and assessed with pattern recognition as the control method. Pre-training and post-training performance for 10 able-bodied participants was evaluated using the Target Achievement Control test for 1, 2 and 3 degrees of freedom. Results showed significant differences in performance before and after 1 hour of desktop training. These results support the hypothesis that a desktop training protocol may improve performance with pattern recognition-based control.
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 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) | 0.000 | 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) | 0.004 | 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".