Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.
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.
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) | 0.001 | 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 it