Novel Grasping Mechanisms of 3D‐Printed Prosthetic Hands
Bibliographic record
Abstract
There are millions of amputees worldwide, and amputation has significantly affected their lives. People with hand losses have difficulties performing the activities of daily living (ADLs). Prosthetic hands have been developed to perform the functions of a human hand by grasping various objects. However, mimicking the actual grasping function of a human hand is still an unresolved research topic. Researchers continue to improve the functionality of hand prostheses to create more efficient and closer to human motion. Herein, the current challenges and approaches for enhancing the grasping function of 3D‐printed hand prostheses are investigated. Three technology sectors are discussed for the efficient grasping motion of hand prosthetics: 1) how to recognize the user's desired grasping gestures by sensing systems; 2) how to power the prosthesis for grasping objects through different actuation systems; and 3) how to perform the grasping motion by 3D design and mechanisms. This article reviews valuable information regarding the current innovations toward improving prosthetic hands’ grasping function to help researchers design more functional hand prostheses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".