Assessment of UAV operator workload in a reconfigurable multi-touch ground control station environment
Bibliographic record
Abstract
Multi-touch computer inputs allow users to interact with a virtual environment through the use of gesture commands on a monitor instead of a mouse and keyboard. This style of input is easy for the human mind to adapt to because gestures directly reflect how one interacts with the natural environment. This paper presents and assesses a personal-computer-based unmanned aerial vehicle ground control station that utilizes multi-touch gesture inputs and system reconfigurability to enhance operator performance. The system was developed at Ryerson University’s Mixed-Reality Immersive Motion Simulation Laboratory using commercial-off-the-shelf Presagis software. The ground control station was then evaluated using NASA’s task load index to determine if the inclusion of multi-touch gestures and reconfigurability provided an improvement in operator workload over the more traditional style of mouse and keyboard inputs. To conduct this assessment, participants were tasked with flying a simulated aircraft through a specified number of waypoints, and had to utilize a payload controller within a predetermined area. The task load index results from these flight tests have initially shown that the developed touch-capable ground control station improved operator workload while reducing the impact of all six related human factors.
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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) | 0.000 | 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) | 0.002 | 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 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".