https://github.com/lucianoayres/motion-ai-game

An AI-based Pose Detection and Action Recognition Game
https://github.com/lucianoayres/motion-ai-game

ai deep-learning game machine-learning pose-detection tensorflowjs webcam

Last synced: 3 months ago
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An AI-based Pose Detection and Action Recognition Game

• Host: GitHub
• URL: https://github.com/lucianoayres/motion-ai-game
• Owner: lucianoayres
• License: mit
• Created: 2024-05-18T01:07:01.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-25T15:38:46.000Z (about 1 year ago)
• Last Synced: 2025-01-18T08:36:34.729Z (5 months ago)
• Topics: ai, deep-learning, game, machine-learning, pose-detection, tensorflowjs, webcam
• Language: CSS
• Homepage: https://lucianoayres.github.io/motion-ai-game/
• Size: 14.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Motion: An AI-based Pose Detection and Action Recogniton Game

This web game leverages deep learning models for pose estimation and action recognition, delivering real-time feedback based on accurately detected poses.

![Motion Game Screenshot](https://github.com/lucianoayres/motion-ai-game/assets/20209393/57749940-b41a-4fc3-a4df-0ce8774bb279)

## Overview

The application integrates the following functionalities:

-   **Pose Estimation:** Utilizes the PoseNet model to estimate human poses in real-time.

-   **Action Recognition:** Employs a pre-trained Teachable Machine classification model to recognize specific actions based on the detected poses.

-   **Interactive Feedback:** Provides interactive feedback by triggering animations and updating scores and actions on the user interface.

## Usage

[Play it online](https://lucianoayres.github.io/motion-ai-game/) or clone the project to run it locally through a live server on a web browser.

Once the application is running, access it through a web browser. The webcam feed will display in real-time, and the application will recognize and respond to different poses and interactions with the 04 colored circles on the screen.

## Technologies Used

-   **HTML, CSS and Javascript**

-   **TensorFlow.js:** Utilized for loading and running the pre-trained machine learning models.

-   **Teachable Machine:** Provides a pre-trained classification model for action recognition.

-   **PoseNet:** Enables real-time human pose estimation from input images or video.

## Contributing

Contributions to the project are welcome. To contribute, follow these steps:

1. Fork the repository.

2. Create a new branch for your feature or bug fix: `git checkout -b feature-name`

3. Make changes and commit them: `git commit -m 'Description of changes'`

4. Push to the branch: `git push origin feature-name`

5. Submit a pull request.

## License

This project is licensed under the [MIT License](LICENSE).

## Author

This project was developed by [Luciano Ayres](https://www.linkedin.com/in/lucianoayres).

## Acknowledgments

-   Special thanks to the developers of TensorFlow.js, Teachable Machine, and PoseNet for their valuable contributions to the machine learning community.