Kinematic design of linkage-based haptic interfaces for medical applications: a review
Bibliographic record
Abstract
<p>A haptic interface recreates haptic feedback from virtual environments or haptic teleoperation systems that engages the user's sense of touch. High-fidelity haptic feedback is critical to the safety and success of any interaction with human beings. Such interactions can be seen in haptic systems utilized in medical fields, such as for surgical training, robotic tele-surgery, and tele-rehabilitation, which require appropriate haptic interface design and control. In order to recreate high-fidelity soft and stiff contact experiences for the user in the intended application, different designs strike different trade-offs between the desirable characteristics of an interface, such as back-drivability, low apparent inertia and low friction for the best perception of small reflected forces, large intrinsic stiffness and force feedback capability for the best perception of large reflected forces, a large-enough workspace for exploring the remote or virtual environment, and the uniformity of haptic feedback and its adequate sensitivity over the workspace. Meeting all of the requirements simultaneously is impossible, and different application-driven compromises need to be made. This paper reviews how various kinematic designs have helped address these trade-offs in desired specifications. First, we investigate the required characteristics of linkage-based haptic interfaces and inevitable trade-offs between them. Then, we study the state of the art in the kinematic design of haptic interfaces and their advantages and limitations. In all sections, we consider the applications of the intended haptic interfaces in medical scenarios. Non-linkage-based haptic interfaces are also shortly discussed to show the broad range of haptic technologies in the area. The potentials of kinematic redundancy to address the design trade-offs are introduced. Current challenges and future directions of haptic interface designs for medical applications are shortly discussed, which is finally followed by the conclusion.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".