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BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition.

Rafael Del-Hoyo-Alonso1, Ana Caren Hernández-Ruiz1, Carlos Marañes-Nueno1

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BodyFlow is a new library for human activity recognition, integrating pose estimation and sensor data. It simplifies identifying activities and body joints from various inputs for diverse applications.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Human activity recognition (HAR) is vital for healthcare, sports, security, and gaming.
  • Existing HAR methods often lack comprehensive integration of pose estimation and multimodal data processing.

Purpose of the Study:

  • To introduce BodyFlow, a unified library for human pose estimation, tracking, and activity recognition.
  • To facilitate multimodal human activity recognition by integrating visual and inertial sensor data.

Main Methods:

  • Developed BodyFlow, a library integrating 2D/3D human pose estimation and multiple-person tracking.
  • Incorporated three distinct models for human activity recognition.
  • Enabled processing of video, image sets, webcam feeds, and inertial sensor data.

Main Results:

  • BodyFlow allows seamless identification of common human activities and 2D/3D body joints.
  • The library supports multimodal input, combining visual and sensor data for enhanced recognition.
  • State-of-the-art algorithms are utilized for pose estimation and activity recognition.

Conclusions:

  • BodyFlow offers a comprehensive and flexible solution for human activity recognition.
  • The library's multimodal capabilities enhance the accuracy and robustness of HAR systems.
  • BodyFlow simplifies the development and deployment of HAR applications across various domains.