TRAIN-KNEE: Developing a Haptic Manikin for Knee Injury Assessment Training
Bibliographic record
Abstract
We present the design and implementation of a high-fidelity haptic manikin for knee injury assessment training. Currently, such training is conducted through direct instruction on live patients or peer-to-peer practice, which may limit exposure to multiple injury severities and raise ethical concerns. Our manikindevice aims to assist inexperienced practitioners in mastering an injury assessment technique specifically for the medial collateral ligament (MCL). We designed the manikin collaboratively with a certified clinician. Our design incorporates a commercial human knee joint model for accurate anatomical representation, materials that closely mimic human skin properties, an injury simulation mechanism for replicating MCL injuries, and pressure sensors to capture user-applied pressure during manipulation. We conducted threetwo evaluations: an internal test with our collaborating clinician to configure our manikin for four MCL injury conditions (i.e., healthy, grade 1, grade 2, and grade 3) using a psychophysics method; a subsequent study where 6 certified clinicians rated each condition for consistency and a technical evaluation measuring abduction range in the healthy and grade 3 configurations. Results show that our manikin can reliably displaydistinguish between healthy and unhealthy MCLs, with a sensitivity of 0.83 and specificity of 1.00 for healthy condition, and 1.00 and 0.83, respectively, for the unhealthy condition. However, further improvements are needed to accurately distinguish between injury gradesgrade 1, grade 2, and grade 3 injuries. Our manikin’s realistic weight and shape were highly praised, but there is room for improvement in simulating the skin texture. This work shows the potential of realistic simulators to enhance clinical training with standardized and repeatable practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".