Error amplification to promote motor learning and motivation in therapy robotics
Bibliographic record
Abstract
To study the effects of different feedback error amplification methods on a subject's upper-limb motor learning and affect during a point-to-point reaching exercise, we developed a real-time controller for a robotic manipulandum. The reaching environment was visually distorted by implementing a thirty degrees rotation between the coordinate systems of the robot's end-effector and the visual display. Feedback error amplification was provided to subjects as they trained to learn reaching within the visually rotated environment. Error amplification was provided either visually or through both haptic and visual means, each method with two different amplification gains. Subjects' performance (i.e., trajectory error) and self-reports to a questionnaire were used to study the speed and amount of adaptation promoted by each error amplification method and subjects' emotional changes. We found that providing haptic and visual feedback promotes faster adaptation to the distortion and increases subjects' satisfaction with the task, leading to a higher level of attentiveness during the exercise. This finding can be used to design a novel exercise regimen, where alternating between error amplification methods is used to both increase a subject's motor learning and maintain a minimum level of motivational engagement in the exercise. In future experiments, we will test whether such exercise methods will lead to a faster learning time and greater motivation to pursue a therapy exercise regimen.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".