Dynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation
Dynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation explores A CNN-based system for dynamic hand gesture recognition using skeletal keypoints, achieving high accuracy for device control.. Commercial viability score: 5/10 in Gesture Recognition.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisSTARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language RecognitionViability: 4.0
- Builds On ThisCross-Hand Latent Representation for Vision-Language-Action ModelsViability: 4.0
- Higher ViabilityLeveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMGViability: 6.0
- Higher ViabilityInterpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio FusionViability: 7.0
- Higher Viability