Do Stroke Survivors Activate Their Muscles to Supplement the Hand Extension Robot Orthosis (HERO) Glove's Assistance?
Bibliographic record
Abstract
The Hand Extension Robot Orthosis (HERO) Glove enables stroke survivors with severe hand impairment to perform activities of daily living more independently. OBJECTIVE: To determine whether stroke survivors show voluntary activation of the forearm muscles while using the HERO Glove. METHODS: Three stroke survivors (Chedoke McMaster Stroke Assessment – Stage of Hand 1-3/7) performed maximum voluntary contractions (MVC), the box and block test and a water bottle grasp and lift task while using the HERO Glove and wearing the Myo Armband for muscle activity measurement. RESULTS: Three stroke survivors with severe hand impairment showed varied levels of muscle activation while using the HERO Glove. One participant with a Stage 3 Hand and strong grip strength (55N) showed generalized activation of the forearm flexor and extensor muscles (33% MVC) while grasping the blocks and water bottle, which supplemented the HERO Glove’s assistance. The participant relaxed the forearm muscles while releasing the blocks and water bottle. One participant with a clenched Stage 3 Hand and weak pinch strength (10N) showed generalized forearm muscle activation (30% MVC) that did not relax while releasing the blocks or water bottle. The participant with a Stage 1 Hand (flaccid paralysis) did not show muscle activation during either task. IMPLICATIONS: This analysis demonstrates that a variety of sensors, control modes and training strategies are required to detect the intent of users at higher and lower stages of recovery. This analysis provides support for using the HERO Glove as an assistive device for stroke survivors at lower stages of recovery and as a neuromuscular rehabilitation tool for stroke survivors at higher stages of recovery.
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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 | 0.002 |
| 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 | 0.000 | 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".