A Natural Bare-Hand Interaction Method With Augmented Reality for Constraint-Based Virtual Assembly
Bibliographic record
Abstract
Traditional virtual assembly methods have a high requirement in hand-eye coordination because of the separation of feedback and operation regions. These methods are inconsistent with human interaction habits and lack of naturalness because of limited interaction space and indirect interaction mode. Therefore, we propose a natural bare-hand interaction method for virtual assembly, enabling operators to interact with virtual objects by using natural gestures even while in motion. The Leap Motion controller (LMC) fixed on the Augmented Reality (AR) glasses is used to track hands of the operator and the mobility of the interactive device relieves the location limitation of assembly processes. Furthermore, AR allows operators to perform bare-hand assembly in a realistic situation. The interval Kalman filter (IKF) is applied to estimate hands’ positions to improve the accuracy of measured gesture data. Moreover, constraint assisted technology is introduced to aid operators in learning and completing assembly tasks quickly and accurately, and the assembly sequence is generated to assist the decision-making process during the interaction. Experimental results show that the proposed method can be used by non-professional operators for assembly tasks and can potentially improve assembly efficiency. Based on operator ratings about assembly experience and significant difference analysis, the proposed method performed better at providing better interactive experiences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".