Haptics to improve task performance in people with disabilities: A review of previous studies and a guide to future research with children with disabilities
Bibliographic record
Abstract
This review examines the studies most pertinent to the potential of haptics on the functionality of assistive robots in manipulation tasks for use by children with disabilities. Haptics is the fast-emerging science that studies the sense of touch concerning the interaction of a human and his/her environment; this paper particularly studies the human-machine interaction that happens through a haptic interface to enable touch feedback. Haptics-enabled user interfaces for assistive robots can potentially benefit children whose haptic exploration is impaired due to a disability in their infancy and throughout their childhood. A haptic interface can provide touch feedback and potentially contribute to an enhancement in perception of objects and overall ability to perform manipulation tasks. The intention of this paper is to review the research on the applications of haptics, exclusively focusing on attributes affecting task performance. A review of studies will give a retrospective insight into previous research with various disability populations, and inform potential limitations/challenges in research regarding haptic interfaces for assistive robots for use by children with disabilities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".