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MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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A multimedia art interaction model for motion recognition based on data-driven model.

Zhen Wang1

  • 1Henan Economy and Trade Vocational College, Zhengzhou, Henan, China.

Peerj. Computer Science
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances multimedia art design by improving human-computer interaction efficiency through high-precision human motion recognition. A novel multimodal fusion model achieves 97.85% accuracy, surpassing traditional methods.

Keywords:
Action recognitionHuman–computer interactionMultimedia Art

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Area of Science:

  • Human-computer interaction
  • Multimedia art design
  • Ergonomics

Background:

  • Ergonomics significantly impacts user experience in multimedia art.
  • Improving human-computer interaction efficiency is crucial for advanced design.
  • Existing methods for human motion recognition have limitations.

Purpose of the Study:

  • To develop a data-driven neural network model for high-precision human motion recognition.
  • To enhance human-computer interaction efficiency in multimedia art design.
  • To establish a multimodal fusion model for accurate human activity recognition.

Main Methods:

  • Extracted human motion skeleton information using the OpenPose framework from video data.
  • Calculated exercise intensity using inertia data from wearable bracelets.
  • Employed a recurrent neural network (RNN) to fuse video and wearable data for motion recognition.

Main Results:

  • Achieved a human motion recognition precision of 97.85%.
  • Demonstrated superior performance compared to backpropagation neural network (94.35%) and K-nearest neighbor (90.12%).
  • Validated the effectiveness of the multimodal fusion approach.

Conclusions:

  • The proposed multimodal fusion model offers robust technical support for multimedia art interaction design.
  • The high recognition accuracy significantly improves human-computer interaction efficiency.
  • This approach represents an advancement in data-driven human activity recognition.