Hand Extension Robot Orthosis (HERO) Glove: Development and Testing With Stroke Survivors With Severe Hand Impairment
Bibliographic record
Abstract
The hand extension robot orthosis (HERO) glove was iteratively designed with occupational therapists and stroke survivors to enable stroke survivors with severe hand impairment to grasp and stabilize everyday objects, while being portable, lightweight, and easy to set up and use. The robot consists of a batting glove with artificial tendons embedded into the glove's fingers. The tendons are pulled and pushed by a linear actuator to extend and flex the fingers. The robot's finger extension and grasp assistance are automated using inertial measurement unit signal thresholds. Five stroke survivors (Chedoke McMaster Stroke Assessment - Stage of Hand 1-3) put on the HERO Glove in 1-3 minutes, with assistance. The stroke survivors performed significantly better on the Box and Block Test (2.8 more blocks transferred, ) while wearing the HERO Glove than when not wearing the glove. Four stroke survivors could only transfer blocks while wearing the HERO Glove. The HERO Glove enabled these stroke survivors to more fully extend their index finger (an increase of 97.5°, ) and three of five stroke survivors were better able grasp a water bottle. Therapists and stroke survivors suggested increasing the HERO Glove's grip force assistance and valued the glove's portability, lightweight design and potential usefulness in assisting with task-based therapy.
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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".