Smart Material Robotic Technologies For Assisting Individuals with Upper Extremity Motor Impairment
Bibliographic record
Abstract
I envision a unified network of intelligent and mind-controlled robotic technologies for assisting individuals with motor impairments that are portable, wearable, minimal in size and affordable.They will: Continuously monitor health conditions; Assess recovery progress; Autonomously administer high-dose rehabilitation interventions to groups of patients in their homes; and Assist with independent living.Currently, my research program primarily focuses on upper extremities (UE).I propose the following objectives: 1. Technology -Develop an innovative, UE close-fitting assistive sleeve based on transformative smart materials and robotics that detects bio-signals, and accordingly, promotes UE movements.2. Interventions -Validate novel rehabilitation interventions enabled by the new technology to facilitate UE motor recovery in individuals with stroke.3. Mechanisms -Monitor their brain reorganization and unveil mechanisms underlying recovery of motor function.4. Assistance -Provide evidence that the robotic sleeve enhances independence.5. Translation -Determine research priorities and implement integrated knowledge translation through direct involvement of stakeholders in all phases of the research.By advancing knowledge in neurorehabilitation, and validating novel rehabilitation strategies, significant steps will be made to improve the health and quality of life of individuals living with stroke, which represents over 62 million people worldwide.[1]Furthermore, by facilitating recovery and enabling independent living, the long-term benefit of this research will be a reduced economic burden for both the people in need of assistance and the healthcare system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".