Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Bibliographic record
Abstract
This work contributes a conceptual analysis of upper-limb prosthesis control methods; the goal of this work is to deliver new insight into the design of future biomechatronic systems intended for human interaction. Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by a user's robotic limb. However, controlling multiple degrees of freedom via muscle-generated (myoelectric) signals remains challenging for individuals with limb difference. To address this issue, various machine learning controllers have been developed to better predict a user's movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. We here suggest that one possible approach to improve the under-standing of these interfaces is to model them as collaborative, multi-agent systems through the lens of human-prosthesis joint action. The field of joint action has been commonly applied to two human partners who work jointly together to effect coordinated change in their shared environment. Using a joint action framework also provides opportunities to understand the interactions between human and machine partners: how each represents the other's goal, their monitoring and prediction of each other's actions, the communication between them, and their ability to adapt to each other. In this work, we survey three different prosthesis controllers-proportional electromyo-graphy with sequential switching, pattern recognition, and adaptive switching-in terms of how they present the hallmarks of joint action. The results of this comparison contribute a new perspective for understanding how existing myoelectric systems relate to each other, along with two concrete recommendations for how to improve these systems via additional capacity for prediction learning and coordination smoothing.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".