Portable Neurorobotics for the Severely Affected Arm in Chronic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Few motor therapies increase active movement in the severely impaired arm of individuals with chronic stroke. Existing robotic devices to address this need are large and expensive. This case study describes the application and reports outcomes associated with a repetitive task-specific training (RTP) program incorporating a portable robotic device. We assessed outcomes related to affected arm impairment, ability to perform valued activities, satisfaction with movement performance, and quality of life in a participant with chronic stroke exhibiting severe arm hemiparesis. CASE DESCRIPTION: The participant was a 53-year-old man, 30 months after hemorrhagic stroke. At the time of enrollment, he exhibited some active shoulder and elbow flexion, but no active elbow extension, and no active movement at any joint below the elbow. INTERVENTION: The participant engaged in RTP incorporating a portable, electromyography-triggered neurorobotic device in 1-hour sessions, 3 days/week for 8 weeks using the affected arm. OUTCOMES: The upper extremity section of the Fugl-Meyer Impairment scale (FM), the Canadian Occupational Performance Measure (COPM), and the Stroke Impact Scale (SIS) were administered before and after training. After intervention, the subject exhibited reduced affected arm impairment (+2 points on the FM), increased ability to perform valued activities, increased satisfaction with performance of these activities (indicated by score increases of +2 and +1.8 points on the COPM Performance and Satisfaction scales, respectively), improved strength, performance of activities of daily living, hand function, participation, and physical function (as indicated by increases in respective SIS scores). DISCUSSION: The RTP incorporating the neurorobotic device appears promising. To our knowledge, this is the first study documenting a portable robotic-based RTP strategy in a person exhibiting this severity of hemiparesis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".