Edge-AI Powered Real-Time Gesture Recognition System for Accessible Human-Computer Interaction
Bibliographic record
Abstract
In order to improve accessible human-computer interaction, this work introduces a real-time gesture recognition system driven by Edge AI. The suggested framework accurately recognizes a variety of gestures, such as pointing, finger configurations, and palm orientations, by utilizing the MediaPipe library for effective hand detection and keypoint estimation. The system is ideal for edge device deployment because it operates on live video streams, captures frames in real-time, processes them using lightweight neural models, and classifies gestures with low latency. In order to provide flexibility, the implementation incorporates argparse modulebased configurable command-line parameters that enable adjusting camera settings, frame resolution, and confidence thresholds for tracking and detection. By combining reliable landmark extraction with gesture classification, the gesture recognition pipeline operates continuously in a closed loop, striking a balance between efficiency, accuracy, and speed. This method reduces latency, improves accessibility in assistive and interactive applications, and does away with reliance on cloudbased processing by directly embedding gesture recognition on edge devices.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".