STRAHAND: Rehabilitation-Oriented Exoskeleton for the Hand
Bibliographic record
Abstract
Rheumatoid arthritis (RA), a chronic autoimmune disease, affects a significant portion of the elderly population and often poses challenges in performing essential activities of daily living. Simple tasks such as grasping an apple or holding cutlery can become arduous and painful for individuals with this condition. Consequently, many of them require rehabilitative procedures to re-establish the functionality of their hands. In this context, the implementation of a glove-based solution that incorporates pneumatic technology holds promising potential. By harnessing the power of pneumatic action, this innovative glove can provide valuable assistance in facilitating the squeezing movement of the hand. This assistance can prove immensely beneficial for individuals struggling with the debilitating effects of (RA). The primary objective of this project is twofold. Firstly, it aims to directly support patients in their daily activities by alleviating the challenges associated with hand movements affected by rheumatoid arthritis. The glove, through its pneumatic action, can enhance grip strength and provide the necessary support to perform tasks that were previously difficult or impossible. Moreover, the project strives to help patients regain lost mobility and independence. By actively assisting hand motions, the glove encourages engagement in rehabilitative exercises, contributing to the gradual restoration of dexterity and motor function. The dual purpose of this innovative glove is to not only offer immediate relief and assistance but also serve as a rehabilitative tool, fostering the long-term recovery of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".