Editorial: Haptic training simulation, volume II
Bibliographic record
Abstract
Haptic training simulation, volume IIHaptic training simulation (Lelevé et al., 2020) usually deals with kinesthetic feedback.This second edition explores complementary approaches in the medical domain (as in He et al. (2022) specifically for tissue examination), to provide realistic feedback and objective assessment to trainees during their training.Thus, Dragunasu et al. and Rørvik et al. introduce novel tactile devices while Gautier et al. propose to equip practice boxes with a vision system to enable objective assessment.From an complementary point of view, Jourdes et al. propose to train on surgical robots that do not provide haptic feedback using visual feedback (Bresler et al., 2020).Simulating effective haptic feedback in a virtual environment is a challenging problem that involves a myriad of design considerations.To improve the wearability of such devices, primary design considerations involve optimizing the footprint of the actuating mechanism on the skin and the number of actuators used to simulate haptic feedback.Using parallel actuation mechanisms to solve the problem of achieving efficient and relevant force-feedback is an actively researched topic.There have been numerous designs that seek to address this issue, but few that involve the palm of hand.Dragunasu et al. propose a novel design for a device that simulates cutaneous feedback at the palm of hand by leveraging grasping biomechanics and using tendon-like mechanical actuation to generate both tangential and normal force feedback at the palm.To show the efficacy of the haptic feedback from the present prototype, the work demonstrates a simple virtual scenario for interacting with objects.Medical palpation training is essential to improve tactile diagnostic skills among professionals, but currently available training equipment lacks commercial viability and richness of tactile stimulation to provide realistic feedback to trainees.To address this problem, it is necessary to effectively simulate the hardness and shape of the
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.034 | 0.031 |
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".