First prototype of EMG-controlled power hand orthosis for restoring hand extension in stroke patients
Bibliographic record
Abstract
Weakness in finger extensors is a common post-stroke deficit that can disturb hand functioning. Despite introducing several powered hand orthoses in literature, most of these devices focused on providing finger flexion. There is a little consideration for providing active hand extension in stroke patients. Moreover, in many devices, the finger extensions were restored passively by spring component. In this study, a new Electromyography (EMG)-controlled powered hand orthosis was designed to improve hand function by restoring and training hand extension in stroke patients with paretic hand. This orthosis was a glove-like device that was developed from two mechanical and electrical sections. After construction and verifying of the orthosis, its applicability was tested on two patients with Cerebrovascular accident (1 woman and 1 man) with paretic hands in an 18-session therapeutic approach. To evaluate the effectiveness of orthosis, Wolf Motor Function Test and Box and Block test were conducted before and after the training sessions. The primary assessment of the prototype was conducted on a healthy subject and three stroke patients. These evaluations showed that the new powered hand orthosis could be effective for finger extension task and training. Furthermore, after the 18-session training approach, significant improvements were seen in the scores of both Wolf and Box and Block tests. The preliminary findings suggested that the first prototype of orthosis could provide a desirable function for stroke patients with paretic hand. Moreover, it could be used as a training device in the rehabilitation of these patients.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".