Myoelectric prosthesis control using recurrent convolutional neural network regression mitigates the limb position effect
Bibliographic record
Abstract
Many myoelectric upper limb prosthesis controllers use pattern recognition, a method that learns and recognizes patterns of electromyographic (EMG) signals produced by the user’s residual limb muscles to predict and execute device movements. Such control becomes unreliable in high limb positions—a problem known as the limb position effect. Pattern recognition often uses a classification algorithm; simple to implement, but limits user-initiated control to only one device movement at a time, at a single speed. To combat position-related control deficiencies and classification controller constraints, we developed and tested two recurrent convolutional neural network (RCNN) pattern recognition-based solutions: (1) an RCNN classification controller that uses EMG plus positional inertial measurement unit (IMU) signals to offer one-speed, sequential movement control; and (2) an RCNN regression controller that uses the same data capture technique to offer simultaneous control of multiple movements and device movement velocity. We assessed both RCNN controllers by comparing them to a commonly used linear discriminant analysis classification controller (LDA-Baseline). Participants without upper limb impairment were recruited to perform multipositional tasks while wearing a simulated prosthesis. Both RCNN classification and regression controllers showed improved functional task performance over LDA-Baseline, in 11 and 38 out of 115 metrics, respectively. This work contributes an RCNN regression-based controller that provides accurate, simultaneous, and proportional movements to EMG-based technologies including prostheses, exoskeletons, and even muscle-activated video games.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".