Initial Investigation of a Self-Adjusting Wrist Control System to Maintain Prosthesis Terminal Device Orientation Relative to the Ground Reference Frame
Bibliographic record
Abstract
Lack of adequate wrist control in prostheses forces people with upper limb amputations to use compensatory movements that eventually result in overuse injuries. This is partly because conventional control of myoelectric wrists involves switching between directly controlling the wrist and fixing the wrist relative to the forearm. We propose that by implementing a wrist that is able to maintain the hand's orientation relative to the ground reference frame, here termed a self-adjusting wrist, users may see benefits in terms of both compensatory movements and ease of control. In this design study, we describe a simple initial implementation of a self-adjusting wrist. We then introduce and compare five control methods for the system. These methods were tested with six able-bodied participants who used a desk-mounted robotic arm to perform an object transfer and manipulation task. Quantitative and qualitative analyses coupled with user feedback suggest that a self-adjusting wrist may reduce task completion time and number of control interactions, and increase user satisfaction compared to conventional switching-based control. Our results indicate that use of a momentary switch to toggle a robotic hand's orientation between being fixed to the ground reference frame and being either fixed to the forearm reference frame or employing direct wrist control may be the best choice for a self-adjusting wrist. More broadly, by considering a wrist that automatically and continually orients itself to the user and their environment, this work contributes insight about how prostheses and other assistive robotic technology may intelligently adapt in real time to support the daily-life tasks faced by their users.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".