HERO Glove Insight: Utilizing Computer Vision and Force Sensors for Object-Specific Force Control
Bibliographic record
Abstract
This research developed and integrated new mechatronic features to the Hand Extension Robot Orthosis (HERO) Glove soft exoskeleton to improve the assistive abilities of the device. The added features include low weight actuators, anchoring structures, force sensors, a camera and a cascaded computer vision driven Proportional Integral Derivative (PID) algorithm. Supporting electronics were developed to ensure that the integrated system can provide accurate grip force control based on situational awareness. The upgraded version of the HERO glove was evaluated and showed a 55% increase in grip strength, improved durability, and new force sensing and control capabilities as accurate as 0.1 Newtons. The algorithm uses computer vision software to recognize the object that the user intends to grip and then adjusts the target grip force commanded during autonomous gripping. This enhanced system, HERO Glove Insight is designed to overcome usability challenges in grasp stability and grip force modulation faced by stroke and spinal cord injury survivors when utilizing soft hand exoskeletons to enable their independence in home and community settings.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".