Telerobotics-Assisted Platform for Enhancing Interaction with Physical Environments for People Living with Cerebral Palsy
Bibliographic record
Abstract
In this paper, the design and implementation of a new telerobotics-assisted platform is proposed for individuals who have cerebral palsy (CP). The main objective of the proposed assistive system is to modulate capabilities of individuals through the proposed telerobotic medium and to enhance their control over interaction with objects in a real physical environment. The proposed platform is motivated by evidence showing that lack of interaction with real environments can develop further secondary sensorimotor and cognitive issues for people who grow up with CP. The proposed telerobotic system assists individuals by (a) mapping their limited but convenient motion range to a larger workspace needed for task performance in the real environment, (b) transferring only the voluntary components of the hand motion to the task-side robot to perform tasks and (c) kinaesthetically dissipating the energy of their involuntary motions using a viscous force field implemented in high frequency domain. Consequently, using the proposed system, an individual who has CP will be capable of providing smooth and large-scale motions and presenting enhanced coordination while performing tasks, even if they naturally have involuntary movements, limited range of motion and/or coordination deficits. The proposed architecture is implemented and initially tested for one nondisabled participant. Afterwards, the system is evaluated for one individual who lives with CP. The resulting quality of motion and task performance are analyzed through a designed clinical protocol. The results confirm the functionality of the proposed assistive platform in enhancing the capabilities of individuals who live with CP in interacting with physical environments.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".