Bernstein’s Levels of Construction of Movements Applied to Upper Limb Prosthetics
Bibliographic record
Abstract
In Brief This article addresses the neuromotor control processes underlying the use of an upper limb prosthesis. Knowledge of these processes is used to make recommendations as to how prostheses and prosthesis training should develop to advance the functionality of upper limb prostheses. Obviously, modern-day prostheses are not optimally integrated in neuromotor functioning. The current article frames the problems underlying the handling of upper limb prosthetic devices in the hierarchical levels of construction of movement as proposed by Bernstein (1996). It follows that 1) postural disturbances resulting from prosthetic use should be considered in training and in the development of prosthetic devices, 2) training should take into account that new synergies have to be learned, 3) the feedback about the state of the prosthesis should improve, and 4) the alteration between different grip patterns should be made easy and fast. We observed that many of the current innovations in the prosthetics field are in line with the aim to integrate the prosthesis in sensory-motor functioning. Neuromotor control processes underlying the use of an upper-limb prosthesis are used to make recommendations as to how prostheses and prosthesis training should develop to advance functionality of upper-limb prostheses. Problems underlying the integration of modern-day prostheses with neuromotor functioning are based on the hierarchical levels of construction of movement as proposed by Nicholas Bernstein. New technological developments are emerging that may lead to prostheses that are better aligned to what is required from a sensory-motor perspective.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".