Piecewise: A Non-Isomorphic 3D Manipulation Technique That Factors Upper-Limb Ergonomics
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Bibliographic record
Abstract
Virtual reality (VR) is gaining popularity as an educational, training, and healthcare tool due to its decreasing cost. Because of the high user variability in terms of ergonomics, 3D manipulation techniques (3DMTs) for 3D user interfaces (3DUIs) must be adjustable for comfort and usability, hence avoiding interactions that only function for the typical user. Given the role of the upper limb (i.e., arm, forearm, and hands) in interacting with virtual objects, research has led to the development of 3DMTs for facilitating isomorphic (i.e., an equal translation of controller movement) and non-isomorphic (i.e., adjusted controller visuals in VR) interactions. Although advances in 3DMTs have been proven to facilitate VR interactions, user variability has not been addressed in terms of ergonomics. This work introduces Piecewise, an upper-limb-customized non-isomorphic 3DMT for 3DUIs that accounts for user variability by incorporating upper-limb ergonomics and comfort range of motion. Our research investigates the effects of upper-limb ergonomics on time completion, skipped objects, percentage of reach, upper-body lean, engagement, and presence levels in comparison to common 3DMTs, such as normal (physical reach), object translation, and reach-bounded non-linear input amplification (RBNLIA). A 20-person within-subjects study revealed that upper-limb ergonomics influence the execution and perception of tasks in virtual reality. The proposed Piecewise approach ranked second behind the RBNLIA method, although all 3DMTs were evaluated as usable, engaging, and favorable in general. The implications of our research are significant because upper-limb ergonomics can affect VR performance for a broader range of users as the technology becomes widely available and adopted for accessibility and inclusive design, providing opportunities to provide additional customizations that can affect the VR user experience.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it