Robotic Rehabilitation and Assistance for Individuals With Movement Disorders Based on a Kinematic Model of the Upper Limb
Bibliographic record
Abstract
Design and development of robotic-assistance must consider the abilities of individuals with disabilities. In this article, a 8-DOF kinematic model of the upper limb complex is derived to evaluate the reachable workspace of the arm during interaction with a planar robot and to serve as the basis for rehabilitation strategies and assistive robotics. Through inverse differential kinematics and by taking account the physical limits of each arm joint, the model determines workspaces where the individual is able to perform tasks and those regions where robotic assistance is required. Next, a learning-from-demonstration strategy via a nonparametric potential field function is derived to teach the robot the required assistance based on demonstrations of functional tasks. This article investigates two applications. First, in the context of rehabilitation, robotic assistance is only provided if the individual is required to move her arm in regions that are not reachable via voluntary motion. Second, in the context of assistive robotics, the demonstrated trajectory is scaled down to match the individual's voluntary range of motion through a nonlinear workspace mapping. Assistance is provided within that workspace only. Experimental results in 5 different experimental scenarios with a person with cerebral palsy confirm the suitability of the proposed framework.
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 | 0.000 | 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 | 0.000 | 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".