Editorial: Haptic training simulation, volume III
Bibliographic record
Abstract
Dual Users. Another way to make use of haptic devices for enhancing hands-on training is illustrated in Zhang et al. In this study, the haptic devices are not used to interact with the objects in a virtual world as in González-Mena and Neri et al., but to reproduce the expert gestures on the hands of the trainees. More precisely, the surgical tools handled by the expert are connected to individual haptic devices, each one recording in real time its connected tool trajectory. These trajectories are sent to the trainees' devices, which, in turn, guide the tools of the trainees. Thus, trainees can follow in their hands the expert tool trajectories, instead of only watching them and reproducing them on their own as usual. This experimental study suggests that haptic feedback superimposed on the trainee's motions can facilitate the performance of novice operators experiencing moments of difficulty, which was something that was already observed in other works (see the discussion of Zhang et al. for references). Even if the small sample size and use of a simple task limit the generalizability of their findings, this study illustrates that haptic training can be realized without any 3D virtual world, which requires accurate modeling for realistic haptic rendering of a complex task. Tactile Feedback. In Ratschat et al., the authors designed a shape exploration experiment to evaluate the effectiveness of multimodal tactile and kinesthetic feedback on shape perception. Sixteen participants were involved to reproduce different two-dimensional shapes with diverse characteristics in free space after exploring the shapes with two haptic feedback conditions: 1) kinesthetic feedback only and 2) kinesthetic plus tactile feedback. The kinesthetic feedback mechanism was implemented through an adapted single-degree-of-freedom SenseGlove Nova mechanism with an integrated electromagnetic brake. And tactile feedback was provided with a cable-driven platform mounted on the fingertip. To measure the participants' ability to perceive and reproduce the rendered shapes, the authors recorded the time participants spent exploring and reproducing the shapes and the error between the rendered and reproduced shapes after exploration and assessed the workload and motivation with questionnaires. Experimental results show that in a virtual shape exploration task without visual feedback, providing tactile and kinesthetic feedback is associated with more accurate and careful shape reproduction compared to exploring shapes with only kinesthetic feedback. Besides, the addition of tactile feedback does not seem to reduce the time spent during exploration, nor does it have an effect on motivation or workload. Thus, combining haptic and kinesthetic feedback could create more realistic virtual environments that may lead to better training results and easier transfer to real-world tasks, having implications across a variety of applications and training scenarios.Vibrotactile. Similarly, in Boutin et al . , the authors combined haptic (vibrotactile) feedback through the use of a haptic glove with a VR simulator for mixed-reality surgical training. They specifically focused on the potential for enhanced sensory feedback within VR. The authors chose to investigate External Ventricular Drain Placement (EVD), a common neurosurgery procedure, as a starting point. Experimental results demonstrated the simulator's accuracy, even though one major limitation was a lack of kinesthetic feedback. Like Ratschat et al., this work shows the potential to create more realistic mixed-reality environments that could extend beyond surgical applications.Kinesthetic. Force feedback plays a vital role in developing surgical skills, yet many virtual reality simulators lack this feature, creating a significant disparity between physical trainers and their digital counterparts, potentially limiting their effectiveness. In Abinaya and Manivannan, the authors take a different approach and use haptic feedback as an assessment metric for surgical training, focussing on laparoscopic surgery using a virtual reality simulator. By incorporating haptic feedback, they replicate the forces between the tool and the tissue, which directly correlate to tissue trauma. A virtual laparoscopic force model is incorporated into the simulator and used to determine the just noticeable differences of the laparoscopic grasping force. The results suggest that a simple linear model is sufficient for gripper force feedback, and a non-linear model does not affect the force perception. Expert laparoscopic surgeons agree that haptic feedback improves learning performance, and the force model improves the accuracy of object interaction during the gripping task.Innovative approaches to enhance surgical training must foster motor and sensory skills while reducing cognitive burden, lowering costs, and being conducive to faster design processes. This collection, the third on the topic of Haptic Training Simulation, underscores the potential of hapticenabled virtual reality tools in shaping the future of surgical education and improving patient outcomes.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.055 | 0.032 |
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".