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Record W4406847980 · doi:10.3389/frobt.2025.1550392

Editorial: Haptic training simulation, volume III

2025· editorial· en· W4406847980 on OpenAlexaff
Carlos Rossa, Xiaojun Chen, Troy McDaniel, Arnaud Lelevé

Bibliographic record

VenueFrontiers in Robotics and AI · 2025
Typeeditorial
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsHaptic technologyComputer scienceKinesthetic learningTask (project management)Generalizability theoryHuman–computer interactionGestureVirtual realityPerceptionRendering (computer graphics)Artificial intelligencePsychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.191
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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