Prosthetic Control Using Multiple Degrees of Freedom of the Shoulder as Inputs and ANNS
Bibliographic record
Abstract
The use of myoelectric signals (MES) for prosthetic control has been in continuous development at the Institute of Biomedical Engineering (IBME) for many years. Although this control scheme has shown to be successful for upper extremity prostheses, it offers no real feedback mechanism which can provide some level of cognitive awareness of the prostheses’ overall performance. D. C. Simpson (1974) developed a control principle which reduces the conscious mental effort demanded while relaying useful feedback information to the user. The principle, which is known as extended physiological proprioception (EPP), uses the position of intact joints as an input signal for prosthetic control. The shoulder joint motions are of particular interest as input signals since they are the foundation upon which most arm motions are initially based and the prosthesis acts as an extension of the residual limb of an amputee. We proposed to use three degrees of freedom of the shoulder (using fibre optic sensors) as possible control inputs for the artificial neural network control system of an above-elbow prosthesis. The goal was to develop artificial neural networks capable of mapping the trajectory of the prosthesis joints for a number of primitive motions found in activities of daily living. With the shoulder joint information available, the control inputs contain much more information and would improve the range of motion accepted by the controller. This should also increase the accuracy of the predicted prosthesis joint positions. Preliminary results indicate that a time delay neural network is a robust architecture for this application.
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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".