Machine learning and unlearning to autonomously switch between the functions of a myoelectric arm
Bibliographic record
Abstract
Powered prosthetic arms with numerous controllable degrees of freedom (DOFs) can be challenging to operate. A common control method for powered prosthetic arms, and other human-machine interfaces, involves switching through a static list of DOFs. However, switching between controllable functions often entails significant time and cognitive effort on the part of the user when performing tasks. One way to decrease the number of switching interactions required of a user is to shift greater autonomy to the prosthetic device, thereby sharing the burden of control between the human and the machine. Our previous work with adaptive switching showed that it is possible to reduce the number of user-initiated switches in a given task by continually optimizing and changing the order in which DOFs are presented to the user during switching. In this paper, we combine adaptive switching with a new machine learning control method, termed autonomous switching, to further decrease the number of manual switching interactions required of a user. Autonomous switching uses predictions, learned in real time through the use of general value functions, to switch automatically between DOFs for the user. We collected results from a subject performing a simple manipulation task with a myoelectric robot arm. As a first contribution of this paper, we describe our autonomous switching approach and demonstrate that it is able to both learn and subsequently unlearn to switch autonomously during ongoing use, a key requirement for maintaining human-centered shared control. As a second contribution, we show that autonomous switching decreases the time spent switching and number of user-initiated switches compared to conventional control. As a final contribution, we show that the addition of feedback to the user can significantly improve the performance of autonomous switching. This work promises to help improve other domains involving human-machine interaction - in particular, assistive or rehabilitative devices that require switching between different modes of operation such as exoskeletons and powered orthotics.
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.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".