Upper limb robot-assisted therapy in subacute and chronic stroke patients using an innovative end-effector haptic device: A pilot study
Bibliographic record
Abstract
BACKGROUND: Significant results have been shown when an upper limb robot-assisted rehabilitation is delivered to stroke patients. OBJECTIVE: To evaluate the effects of upper limb robot-assisted rehabilitation on motor recovery in stroke patients who underwent a treatment based on a haptic device. METHODS: Thirty-nine stroke patients (twenty-three subacute and sixteen chronic) underwent rehabilitation training by using MOTORE/Armotion haptic system. Thirteen healthy subjects were recruited for comparison purpose.The following clinical outcome measures were used: Chedoke-McMaster Stroke Assessment, Modified Ashworth Scale (MAS), Fugl-Meyer Assessment (FM), Medical Research Council, Motricity Index (MI), Box and Block Test (B&B) and Modified Barthel Index (mBI).The following parameters were computed: mean speed, maximum speed, mean time, path length, normalized jerk, mean force, mean error, mean energy expenditure and active patient-robot interaction percentage.The assessments were carried-out before and after treatment. RESULTS: Significant changes were observed in both groups in the FM, MI, B&B and mean speed. Significant changes were observed in mBI, mean time, mean force, mean energy expenditure and active patient-robot interaction percentage in subacute stroke patients. In chronic stroke patients significant changes were found on the MAS-elbow. CONCLUSIONS: The haptic device used is at least as effective as an existing device used in similar studies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".