Bibliographic record
Abstract
Robotic Prosthetic Hands (RPH) are made with the aim of assisting amputees with activities of daily living (ADL). Upper limb amputations lead to physical and mental difficulty because of the many uses of the human hand. Researchers have been working to improve sensory feedback and dexterous manipulation in these devices to improve embodiment and reduce the abandonment rates. Efforts to implement sensory feedback have been explored by researchers in the field of biomedical engineering whereas efforts to improve dexterous manipulation are directed towards the fields of mechanical engineering and computer science. There are numerous problems faced in the field of robotic prostheses such as end point control, sensory feedback, the weight/dexterity dilemma, and the implementation of invasive methods. RPH learning to grasp from touch with tactile sensors is an additional area of research that is promising for robotic grippers. The design of RPH requires multidisciplinary knowledge related to physiology, anatomy, electronics, mechanical design, and software which renders it a complex challenge. This body of work offers a holistic review of the state of the art of current RPH and the subsequent opportunities for advancement in the field.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".