A Deep Learning Framework for Hand Gesture Recognition and Multimodal Interface Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 it