When Less Is More – Discrete Tactile Feedback Dominates Continuous Audio Biofeedback in the Integrated Percept While Controlling a Myoelectric Prosthetic Hand
Bibliographic record
Abstract
State of the art myoelectric hand prostheses can restore some feedforward motor function to their users, but they cannot yet restore sensory feedback. It has been shown, using psychophysical tests, that multi-modal sensory feedback is readily used in the formation of the users' representation of the control task in their central nervous system - their internal model. Hence, to fully describe the effect of providing feedback to prosthesis users, not only should functional outcomes be assessed, but so should the internal model. In this study, we compare the complex interactions between two different feedback types, as well as a combination of the two, on the internal model, and the functional performance of naïve participants without limb difference. We show that adding complementary audio biofeedback to visual feedback enables the development of a significantly stronger internal model for controlling a myoelectric hand compared to visual feedback alone, but adding discrete vibrotactile feedback to vision does not. Both types of feedback, however, improved the functional grasping abilities to a similar degree. Contrary to our expectations, when both types of feedback are combined, the discrete vibrotactile feedback seems to dominate the continuous audio feedback. This finding indicates that simply adding sensory information may not necessarily enhance the formation of the internal model in the short term. In fact, it could even degrade it. These results support our argument that assessment of the internal model is crucial to understanding the effects of any type of feedback, although we cannot be sure that the metrics used here describe the internal model exhaustively. Furthermore, all the feedback types tested herein have been proven to provide significant functional benefits to the participants using a myoelectrically controlled robotic hand. This article, therefore, proposes a crucial conceptual and methodological addition to the evaluation of sensory feedback for upper limb prostheses - the internal model - as well as new types of feedback that promise to significantly and considerably improve functional 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".