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C-MHAD: Continuous Multimodal Human Action Dataset of Simultaneous Video and Inertial Sensing.

Haoran Wei1, Pranav Chopada1, Nasser Kehtarnavaz1

  • 1Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.

Sensors (Basel, Switzerland)
|May 24, 2020
PubMed
Summary
This summary is machine-generated.

A new dataset, Continuous Multimodal Human Action Dataset (C-MHAD), enables realistic human action recognition in continuous streams. Fusing video and inertial data improves recognition accuracy.

Keywords:
fusion of video and inertial sensing for action recognitionpublic domain dataset for multi-modal action recognitionrecognition in continuous action streams

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Existing datasets lack continuous, unsegmented human actions, limiting realistic action recognition research.
  • Current methods struggle with on-the-fly action start/end detection in continuous data streams.
  • No public multimodal dataset simultaneously captures video and inertial data for continuous action streams.

Purpose of the Study:

  • Introduce the Continuous Multimodal Human Action Dataset (C-MHAD) for realistic action recognition.
  • Provide a public domain dataset with simultaneous video and inertial data streams.
  • Evaluate the effectiveness of multimodal fusion for continuous action recognition.

Main Methods:

  • Collected and curated a novel multimodal dataset (C-MHAD) with continuous video and inertial streams.
  • Developed and applied an example action recognition technique utilizing the C-MHAD.
  • Implemented multimodal fusion strategies combining video and inertial data.

Main Results:

  • The C-MHAD dataset enables the study of continuous human action recognition in unsegmented streams.
  • Multimodal fusion of video and inertial data significantly improved action recognition F1 scores.
  • Individual modalities showed lower F1 scores compared to the fused approach.

Conclusions:

  • The C-MHAD dataset addresses a critical gap in realistic human action recognition research.
  • Multimodal data fusion is a promising approach for enhancing continuous action recognition performance.
  • The dataset facilitates further research into on-the-fly action segmentation and recognition.