Human-Robot Interactions - Estimating Force Feedback of the Human Wrist
Bibliographic record
Abstract
Abstract This research is part of the MSK-TIM (Musculoskeletal Tele-robotic Imaging Machine) project. The MSK-TIM is a device which facilitates remote ultrasound diagnosis through remote control of an ultrasound probe. The work reported here is to facilitate human-robot interaction in remote ultrasound imaging; the research work tried to incorporate force feedback into MSK-TIM device such that the operator (radiologist specialist) can examine the patient more efficiently, remotely. Understanding and modeling the biomechanics of the wrist is important in several fields including medicine and robotics. Up to now, most models have been developed to accurately represent the internal structure of the wrist. However, these models are complex and computationally time-consuming. This study reports on investigation on how we developed a time-efficient finite element model of the wrist. The material parameters were derived using model-correction finite element analysis. The force-displacement experimental measurements were taken at three areas of the human wrist using a robotic tele-sonography manipulator. The finite element model was then incrementally modified to improve the computational time, while measuring the corresponding error. The finite element model successfully predicted force feedback from the human arm with a small computation time (18.4s) per step. The subsequent finite element models further reduced this time (0.95s). As a result of this study, it has been shown that soft tissues can be generalized as a phenomenological material to decrease model complexity. Finally, the geometry of anatomy can be simplified, without a major reduction in accuracy, to greatly reduce the computation time — as shown by the 95% reduction in this study.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".