Preliminary testing by adults of a haptics-assisted robot platform designed for children with physical impairments to access play
Bibliographic record
Abstract
Development of children's cognitive and perceptual skills depends heavily on object exploration and experience in their physical world. For children who have severe physical impairments, one of the biggest concerns is the loss of opportunities for meaningful play with objects, including physical contact and manipulation. Assistive robots can enable children to perform object manipulation through the control of simple interfaces. Touch sensations conveyed through haptic interfaces in the form of force reflection or force assistance can help a child to sense the environment and to control a robot. A robotic system with forbidden region virtual fixtures (VFs) was tested in an object sorting task. Three sorting tasks-by color, by shape, and by both color and shape-were performed by 10 adults without disability and one adult with cerebral palsy. Tasks performed with VFs were accomplished faster than tasks performed without VFs, and deviations of the motion area were smaller with VFs than without VFs. For the participant with physical impairments, two out of three tasks were slower with the VFs. This implies that forbidden region VFs are not always able to improve user task performance. Alignment with an individual's unique motion characteristics can improve VF assistance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".