Design and Preliminary Validation of Grasp Assistive Device for an Industrial Environment
Bibliographic record
Abstract
Abstract Carpal tunnel syndrome and tendonitis are two common upper extremity cumulative trauma disorders (CTD) related to repetitive and forceful activities in industrial environments. Reducing the muscular force during activities such as the operation of a pistol grip hand tool could result in lower incidence of CTDs. The objective of this research was to reduce the muscular contribution to the grip force using an active grasp assist orthosis system. A novel soft, pneumatic grasp assistive device was designed to augment the users' strength during operation of pistol grip hand tool. The optimized design was fabricated using rapid prototyping. Device effectiveness was quantified by measuring muscle activity and grip force during an in vivo study of a common industrial activity. Nine subjects experienced with power tools employed by an automobile manufacturer installed 18 fasteners using a pistol grip DC tool with and without the grasp assistive device. Surface electromyography (sEMG) was used to measure the activity of four muscles commonly associated with grasping. The grasp assist significantly reduced the mean, combined, normalized muscle activity by 18% (p<0.05). Muscle activation results were contextualized using the revised strain index (RSI). The grasp assistive device trial yielded a significantly lower mean RSI value than the typical trial by 13% (p<0.05). Using an active grasp assist orthosis could reduce the incidence of CTDs in able bodied industrial workers using DC hand tools. The device used a unique design of sinusoidal bellows oriented at 45 deg to the plane of symmetry that yielded higher force output and a lower bending ratio and lower input pressure with acceptable device rigidity and strength.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".