Exploring the impact of myoelectric prosthesis controllers on visuomotor behavior
Bibliographic record
Abstract
BACKGROUND: Prosthesis users often rely on vision to monitor the activity of their prosthesis, which can be cognitively demanding. This compensatory visual behaviour may be attributed to an absence of feedback from the prosthesis or the unreliability of myoelectric control. Unreliability can arise from the unpredictable control due to variations in electromyography signals that can occur when the arm moves through different limb positions during functional use. More robust position-aware control systems have been explored using deep learning methods, specifically ones that utilize data from different limb positions, that show promising improvements in control characteristics. However, it is unclear how these novel controllers will affect visuomotor behaviour. Specifically, the extent to which control interventions can influence gaze behaviours remain unknown, as previous studies have not yet demonstrated the sensitivity of eye metrics to these interventions. This study aims to explore how visuomotor behaviours change when individuals operate a simulated myoelectric prosthesis using a standard control strategy compared to a position-aware control strategy. METHODS: Participants without limb difference tested two control strategies in a within-subject crossover study design. They controlled a simulated myoelectric prosthesis using a standard control strategy and an advanced position-aware control strategy designed to address the limb position effect. The order in which these control strategies were evaluated was randomized. Eye tracking and motion capture data were collected during functional task execution to assess if using the position-aware control strategy changed visuomotor behaviour compared to the standard controller. RESULTS: There was less visual fixation on the prosthetic hand in the fully extended and cross-body arm position when using the position-aware controller compared to the standard controller. These changes were associated with shorter grasp phase duration and increased smoothness of prosthesis movements. These findings indicated that using the position-aware control strategy may have resulted in less reliance on vision to monitor the prosthesis actions in limb positions where they had better prosthesis control. CONCLUSIONS: This research suggests that visuomotor metrics may be sensitive to prosthesis control interventions, and therefore the use of eye tracking should be considered for performance assessment of prosthesis control.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".