Motion-path based in car gesture control of the multimedia devices
Bibliographic record
Abstract
The goal of this work is to create a simple and intuitive interaction scheme for the in-car multimedia devices. In fact, we propose a hand gestures control that aims to minimize the users distraction while driving. We devise a set of gesture vocabulary for a the multimedia device interaction. Users hand gestures are recognized by capturing the motion-path while they draw different symbols in the air. In order to capture the motion-path, we use Microsoft Kinect camera's 3D body tracking capability. As the camera tracks user's hands, it produces a sequence of motion-points of the body joints, which are then analyzed syntactically to recognize the intended hand gestures. The recognized gesture is further used to interact with the in-car multimedia devices for accessing various entertainment services. Browsing media playlists, changing the track of the audio player, and playing/pausing the media are few examples for which we have integrated the gesture-based interaction. The interaction scheme devoid of any graphical user interface, rather incorporates haptic and audio modality to provide selection feedbacks to the user. Our experiment shows that the proposed gesture recognition technique is robust and its simplified interaction scheme in the automobile environment is interesting and appealing to the people.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".