LeapBoard: Integrating a Leap Motion Controller with a Physical Keyboard for Gesture-Enhanced Interactions
Bibliographic record
Abstract
Abstract This research explores multimodal interaction whereby users engage multiple input and output channels, such as gestures, touch, audio, and graphics. Our system, LeapBoard, is a novel computer keyboard integrating a Leap Motion Controller (LMC) on a 3D printed frame. LeapBoard combines cursor positioning using mid-air gestures with selection using a key on a physical keyboard. In a user study with 12 participants using a 2D Fitts’ law target selection task, we compared LeapBoard with two alternative input methods, (i) a touchpad and (ii) an LMC combining mid-air gestures for cursor movement with a tap gesture for selection. LeapBoard yielded a significantly higher throughput (3.55 bps) than the touchpad (2.26 bps) and the LMC (1.97 bps). Error rates were significant lower for LeapBoard (4.6%) and the touchpad (3.4%) in comparison to LMC (12.8%). As well, the LMC method fared poorly on participant scores for fatigue (6.4/10). Fatigue scores were favorable for LeapBoard (1.8/10) and the touchpad (2.0/10). This work opens new possibilities for gestural and multimodal interaction while demonstrating off-loading of selection to alternate channels such as a separate hand or a dedicated key.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".