Development of a robotic device for facilitating learning by children who have severe disabilities
Bibliographic record
Abstract
This paper presents technical aspects of a robot manipulator developed to facilitate learning by young children who are generally unable to grasp objects or speak. The severity of these physical disabilities also limits assessment of their cognitive and language skills and abilities. The CRS robot manipulator was adapted for use by children who have disabilities. Our emphasis is on the technical control aspects of the development of an interface and communication environment between the child and the robot arm. The system is designed so that each child has user control and control procedures that are individually adapted. Control interfaces include large push buttons, keyboards, laser pointer, and head-controlled switches. Preliminary results have shown that young children who have severe disabilities can use the robotic arm system to complete functional play-related tasks. Developed software allows the child to accomplish a series of multistep tasks by activating one or more single switches. Through a single switch press the child can replay a series of preprogrammed movements that have a development sequence. Children using this system engaged in three-step sequential activities and were highly responsive to the robotic tasks. This was in marked contrast to other interventions using toys and computer games.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".