An intelligent gesture interface for controlling TV sets and set-top boxes
Bibliographic record
Abstract
The control of computers and electronics through hand gestures has gained significant industry and academic attention lately for the usability benefits and convenience that it offers users. Of particular research interest has been the control of living room environments containing televisions and set-top boxes. However, existing research has failed to provide a flexible solution for controlling such devices by hand gestures. They have used cameras that are sensitive to environmental factors such as lighting or that have unreasonable calibration demands. Additionally, the gesture processing techniques used so far have imposed considerable computational burden and have not provided a consistent and compelling TV control experience for a large variety of users and their homes. In this paper, the data returned from a custom 3D depth camera and a customizable gesture language is used to create an intelligent gesture interface for the control of TVs and set-top boxes. By using an infrared blaster to emit the commands typical of a physical remote, any television set or set-top box can be controlled to perform actions such as turning the TV on, changing the volume, muting the sound or changing the channel. Finally, a test setup is presented where a common television and a satellite receiver are controlled exclusively through hand gestures.
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 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".