A Deep Learning Framework for Hand Gesture Recognition and Multimodal Interface Control
Bibliographic record
Abstract
Hand gesture recognition (HGR) is an essential technology with applications spanning human-computer interaction, robotics, augmented reality, and virtual reality.This technology enables more natural and effortless interaction with computers, resulting in an enhanced user experience.As HGR adoption increases, it plays a crucial role in bridging the gap between humans and technology, facilitating seamless communication and interaction.In this study, a novel deep learning approach is proposed for the development of a Hand Gesture Interface (HGI) that enables the control of graphical user interfaces without physical touch on personal computers.The methodology encompasses the analysis, design, implementation, and deployment of the HGI.Experimental results on a hand gesture recognition system indicate that the proposed approach improves accuracy and reduces response time compared to existing methods.The system is capable of controlling various multimedia applications, including VLC media player, Microsoft Word, and PowerPoint.In conclusion, this approach offers a promising solution for the development of HGIs that facilitate efficient and intuitive interactions with computers, making communication more natural and accessible for users.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".