A Multimodal Direct Gaze Interface for Wheelchairs and Teleoperated Robots
Bibliographic record
Abstract
Gaze-based interfaces are especially useful for people with disabilities involving the upper limbs or hands. Typically, users select from a number of options (e.g. letters or commands) displayed on a screen by gazing at the desired option. However, in some applications, e.g. gaze-based driving, it may be dangerous to direct gaze away from the environment towards a separate display. In addition, a purely gaze based interface can present a high cognitive load to users, as gaze is not normally used for selection and/or control, but rather for other purposes, such as information gathering. To address these issues, this paper presents a cost-effective multi-modal system for gaze based driving which combines appearance-based gaze estimates derived from webcam images with push button inputs that trigger command execution. This system uses an intuitive "direct interface", where users determine the direction of motion by gazing in the corresponding direction in the environment. We have implemented the system for both wheelchair control and robotic teleoperation. The use of our system should provide substantial benefits for patients with severe motor disabilities, such as ALS, by providing them with a more natural and affordable method of wheelchair control. We compare the performance of our system to the more conventional and common "indirect" system where gaze is used to select commands from a separate display, showing that our system enables faster and more efficient navigation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".