A practical hand tracking engine.

Note: Yoha is currently unmaintained.

Quick Links:

• Demo
• Code
• Docs
• Website
• npm

Installation

To get started with Yoha, you need to install the library via npm:

npm install @handtracking.io/yoha

Please note the following:

• You need to serve the files from node_modules/@handtracking.io/yoha since the library needs to download the model files from here. Webpack Example
• You need to serve your page with https for webcam access. Webpack Example
• You should use cross-origin isolation as it improves the engine’s performance in certain scenarios. Webpack Example

Description

Yoha is a hand tracking engine built to provide practical solutions for applications that require hand tracking. It aims to be a versatile tool by focusing on specific hand poses that users and developers find beneficial. By detecting these poses, the engine allows for meaningful interactions in applications. To see Yoha in action, check out the demo.

Yoha is currently in beta.

About the Name

Yoha is short for Yobur Hand Tracking.

Language Support

Currently, Yoha is available for the web via JavaScript. More languages can be added in the future. If you’re interested in porting Yoha to another language and need assistance, feel free to reach out!

Technical Details

Yoha is built from scratch using a custom neural network trained with a unique dataset. The inference in the browser is powered by TensorFlow.js.

Features:

• Detection of 21 2D-landmark coordinates (single hand).
• Hand presence detection.
• Hand orientation (left/right hand) detection.
• Inbuilt pose detection.

Supported Hand Poses:

• Pinch (index finger and thumb touch)
• Fist

If your desired pose isn’t listed, feel free to create a new issue here.

Performance

Yoha prioritizes performance, providing a real-time user experience across a broad range of laptops and desktop devices. However, performance on mobile devices may be less optimal, which hopefully will improve with further development of inference frameworks like TensorFlow.js.

Keep in mind that native inference speed can’t be compared with the web inference speed. If you were to run Yoha natively, it would be significantly faster than running it through a web browser.

Minimal Example

• Source
• Running locally:

  git clone https://github.com/handtracking-io/yoha  
  cd yoha/example  
  yarn  
  yarn start

Drawing Demo

• Live Version
• Source
• Running locally:

  git clone https://github.com/handtracking-io/yoha  
  cd yoha  
  ./download_models.sh  
  yarn  
  yarn start

Troubleshooting Tips

If you encounter any issues while setting up or using Yoha, consider the following troubleshooting tips:

• Ensure that you’re serving your application over HTTPS to allow webcam access.
• Check that you have installed all required dependencies from the node_modules directory.
• Verify your cross-origin isolation settings to enhance performance.

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