Kinematic Optimization and Comparison of Wheelchair-mounted Assistive Robots for Activities of Daily Living
Bibliographic record
Abstract
The number of wheelchair users has increased at an alarming rate throughout the last decade due to the increased number of individuals with upper/lower extremity dysfunctions resulting from a stroke, spinal cord injury, trauma, accident, or workplace injuries. Wheelchair-mounted assistive robots have been playing an important role in assisting the activities of daily living (ADLs) of these individuals. This research focuses on the design optimization of wheelchair-mounted assisted robots for essential ADLs. The genetic algorithm (GA) was used to estimate the link length parameters of 5 degrees of freedom (DoFs), 6DoFs, and 7DoFs wheelchair-mounted robots to cover the seven workspaces commonly used in ADLs. The objective function used in the GA includes singularity avoidance, collision avoidance, and ADL coverage. The results show that the proposed optimized 5DoFs robot can cover 25%, 6 DoFs robot can cover 69%, and 7 DoFs robot can cover 71% of essential ADL workspaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".