Examining the feasibility of face gesture detection using a wheelchair mounted camera
Bibliographic record
Abstract
he user interface of existing autonomous wheelchairs concentrates on direct control of the wheelchair by the user using mechanical devices or various hand, head or face gestures. However, it is important to monitor the user to ensure safety an comfort of the user, who operates the autonomous wheelchair. In addition, such monitoring of a user greatly improves usablity of an autonomous wheelchair due to the improved communication between the user and the wheelchair. This paper proposes a user monitoring system for an autonomous wheelchair. The feedback of the user and the information about the actions of the user, obtained by such a system, will be used by the autonomous wheelchair for planning of its future actions. As a first step towards creation of the monitoring system, this work proposes and examines the feasibility of a system that is capable of recognizing static facial gestures of the user using a camera mounted on a wheelchair. The prototype of such a system has been implemented and tested, achieving 90% recognition rate with 6% false positive and 4% false negative rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".