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Project is combintion of software and hardware and Video Call Website will be live on Meet.risg.in

Video Call Intercom System with Sensors and SOS ALerts

This project aims to create an accessible video call intercom system for Deaf individuals, utilizing an analog/IP-based communication system with integrated vibration sensors. The system facilitates communication in environments like offices, homes, and businesses without relying on voice-based communication methods.

Features.

• Video Communication: Facilitates communication using video, making it ideal for sign language users.
• Vibration Alerts: Integrated vibration sensors notify the user of incoming calls or messages.
• Analog/IP System: Offers flexibility in deployment across different infrastructures, from older analog systems to modern IP networks.
• User-Friendly Interface: Designed with simplicity in mind, allowing easy use by individuals who are Deaf or have hearing impairments.

Table of Contents

• Project Description
• Features
• Technologies
• Installation
• Usage
• Contributing
• License
• Contact

Project Description

The Smart India Hackathon project aims to create an interactive hand gesture recognition system. This system leverages computer vision and machine learning to detect hand gestures using a webcam and provides real-time feedback. The project consists of several components:

1. Python Script: Detects and interprets hand gestures.
2. Arduino Script: Interfaces with the Python script for additional functionalities.
3. Website Content: Provides a web-based interface to interact with the system.

Features

• Real-time hand gesture recognition using a webcam.
• Support for recognizing multiple hand gestures (e.g., A, B, C, D, E, F, G).
• Integration with Arduino for extended functionality.
• Web interface for user interaction.

Technologies

• Python: For gesture recognition using OpenCV and MediaPipe.
• Arduino: For interfacing and additional functionalities.
• HTML/CSS: For creating the web interface.
• OpenCV: For image processing.
• MediaPipe: For hand landmark detection.

Installation

Prerequisites

• Python 3.x
• OpenCV
• MediaPipe
• Arduino IDE

Python Setup

1. Clone the repository:

   git clone https://github.com/erpranjalmishra/helixom.git

2. Navigate to the project directory:

   cd helixom

3. Install the required Python libraries:

   pip install opencv-python mediapipe

4. Run the Python script:

   python Python Script/main.py

Arduino Setup

1. Open the Arduino IDE and upload the provided Arduino script located in the Arduino Script folder.

2. Ensure that the Arduino board is properly connected to your computer.

Website Setup

1. Open the Website Content/Index.html file in a web browser to view the web interface.

Usage

1. Running the Python Script: Execute the Python script to start the hand gesture recognition system. The detected gestures will be displayed on the video feed from your webcam.

2. Arduino Integration: The Arduino script will interact with the Python script, providing additional functionalities such as controlling external hardware based on detected gestures.

3. Web Interface: Access the Index.html file to interact with the system via a web-based interface.

Contributing

We welcome contributions to this project! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes and commit them.
4. Push your changes to your forked repository.
5. Open a pull request with a clear description of your changes.

License

This project is licensed under the GNU LESSER GENERAL PUBLIC LICENSE Version 2.1, February 1999 . See the LICENSE file for details.

Contact

For any questions or further information, please contact:

• Project Owner: Team-V
• x@risg.in