A Mobile Natural Human–Robot Interaction Method for Virtual Chinese Acupuncture
Bibliographic record
Abstract
Most of the existing virtual acupuncture methods cannot allow operators to interact naturally in a large area. To address this problem, this article proposes a mobile natural human–robot interaction interface for virtual Chinese acupuncture teaching, including an automatic hand tracking method and an acupuncture interaction method combining vision and force feedback. The automatic hand tracking method can automatically track and capture the operator’s hand movements, allowing the operator to perform acupuncture in a large area. The acupuncture interaction method combining vision and force reproduces the real acupuncture operation mode. The proposed interaction method provides the operator with visual feedback and force feedback without limitation, which improves the naturalness and authenticity of the operation. Through a series of experiments, the proposed method was proven to be more natural and efficient in the virtual acupuncture teaching scene from the point view of interaction. The operators achieve a stronger sense of immersion using this method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".