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Smart Glove for Sri Lankan Sign Language Recognition

The Smart Glove Mobile Application is part of the Smart Glove for Sri Lankan Sign Language Recognition system. The application connects with a sensor-based smart glove and converts detected hand gestures into meaningful communication outputs.

The main goal of this project is to assist speech-impaired individuals by translating Sri Lankan Sign Language gestures into understandable text or voice through a mobile interface.

The smart glove detects hand gestures using sensors and sends the data to the mobile application, where the gestures are processed and displayed as readable text.

🎯 Purpose of the Application

This mobile application acts as the communication interface between the smart glove and the user.

The app is responsible for:

  • Receiving gesture data from the glove
  • Interpreting gesture inputs
  • Displaying translated messages
  • Providing a simple and accessible interface for communication

⚙️ Application Features

  • 📱 Mobile interface for gesture translation
  • 🔗 Bluetooth connectivity with the Smart Glove hardware
  • ✋ Gesture recognition display
  • 🌐 Voice output support for Tamil and Sinhala languages
  • 📊 Gesture data handling and processing
  • 🔤 Add new phrases for easy communication in both local languages and edit them as needed
  • 🔥 Firebase integration for backend services

🛠️ Technologies Used

Mobile Application:

  • React Native
  • Expo
  • TypeScript / JavaScript

Backend:

  • Firebase Authentication
  • Realtime Database / Cloud services

Development Tools:

  • VS Code
  • Git & GitHub

🧤 Smart Glove Hardware (Brief Overview)

The smart glove is a wearable device designed to detect hand movements and finger bending.

Main Hardware Components

  • Flex Sensors – Detect finger bending and hand gestures
  • Microcontroller (Arduino / ESP32) – Processes sensor data
  • Bluetooth Module – Sends gesture data to the mobile application
  • Power Supply – Provides power for the glove components

The glove captures the gesture data and transmits it wirelessly to the mobile application for interpretation.

👨‍💻 Author

Deshanth Vishvalingam Undergraduate – Networking & Mobile Computing

GitHub: https://github.com/Desh07

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