Hand Sentinel: Computer Vision Engine ✋🤖

Hand Sentinel is an advanced Computer Vision and Neural Network engine developed in Python. It utilizes robust Deep Learning models to dynamically map 21 articular nodes on both the left and right hands in real-time. By accurately classifying complete physical signs and complex gestures, the neural network acts as an automated trigger to execute and control media (video playback) without physical interfaces.

✨ Core Features & Architecture

• Bilateral Node Tracking: Precise, real-time spatial mapping of 42 total articular nodes (21 per hand) utilizing advanced neural network architectures.
• Gesture-Triggered Automation: Translates specific, complete physical signs into executable binary commands to instantly trigger and manage video playback.
• Low Latency Processing: Highly optimized for real-time execution and zero-lag rendering via OpenCV frame processing.
• Modular Engineering: Clean, scalable architecture strictly separated into core (AI engine), ui (interface), and assets (media and data) for seamless future integrations.

🛠 Technologies Used

• Core Language: Python 3.x
• Computer Vision: OpenCV, MediaPipe (Hand Tracking Solutions)
• Machine Learning / AI: Integrated Neural Network logic for gesture classification
• Architecture: Modular component isolation and real-time data streaming.

🚀 Installation & Setup

To deploy the Hand Sentinel engine locally on a Windows environment:

1. Clone the repository:

   git clone https://github.com/jp-software-dev/hand-sentinel.git