Does robotic guidance influence the use of proprioception
Bibliographic record
Abstract
There is a growing interest to use robotic guidance in neurorehabilitation settings. One reason could be that it offers highly reliable proprioceptive feedback. Recently, we observed that healthy individuals exhibit more symmetric discrete reaching movements after being exposed to robotic guidance. However, it was not clear if these trajectory symmetry effects were associated with more proprioceptive feedback use or more movement planning. In this study, we sought to determine if trajectory symmetry during physical guidance could promote proprioceptive feedback use. Participants completed 210 training trials to 3 targets (27, 30, 33 cm), either manually aiming to the target, or led through a symmetric or an asymmetric velocity profile provided by a robot arm. All participants completed 10 baseline trials, the training phase, and 20 post-test trials. The post-test included 10 trials with tendon vibration of the biceps brachii tendon during the movement and 10 control trials. Vibration and control trials were blocked and counterbalanced across participants. As expected, a significant target undershoot was observed when vibration was applied. However, this bias did not differ across groups, indicating that robotic guidance did not promote the use of proprioceptive feedback in healthy individuals. Thus, robot-guided motor skill acquisition may not modulate the use of online proprioceptive feedback.Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council (NSERC), the Canada Foundation for Innovation (CFI) and the Ontario Research Fund (ORF).
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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 | 0.006 |
| 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) | 0.004 | 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".