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Continuous dynamic gesture spotting algorithm based on Dempster-Shafer Theory in the augmented reality human computer

Qiming Li1,2, Chen Huang3, Zhengwei Yao4

  • 1Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS
|June 30, 2018
PubMed
Summary

This study introduces a new algorithm for continuous dynamic gesture spotting in augmented reality (AR) human-computer interaction (HCI). The proposed method accurately identifies gestures, improving subsequent recognition and enabling more natural AR experiences.

Keywords:
HCIaugmented realitydynamic gesture spottingevidence reasoning

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Area of Science:

  • Human-Computer Interaction (HCI)
  • Augmented Reality (AR) Technology
  • Gesture Recognition

Background:

  • Naturalness is a key trend in AR human-computer interaction (HCI).
  • Gestures are a natural interaction method, but often contain meaningless intervals.
  • Continuous dynamic gesture spotting is crucial for accurate gesture recognition in AR.

Purpose of the Study:

  • To develop a general AR application development platform for natural HCI.
  • To propose a robust algorithm for continuous dynamic gesture spotting.

Main Methods:

  • Utilized hand position and pose tracking data.
  • Developed a dynamic gesture spotting algorithm based on evidence theory and Dempster-Shafer theory.
  • Analyzed hand motion speed changes to establish knowledge rules for gesture detection.
  • Ensured real-time performance by detecting gesture start points during speed increases.

Main Results:

  • The dynamic gesture spotting algorithm achieved satisfactory accuracy with six users.
  • Gesture recognition accuracy improved when spotting preceded recognition.
  • The algorithm effectively extracts dynamic gestures for AR HCI.

Conclusions:

  • The proposed continuous dynamic gesture spotting algorithm enhances AR HCI.
  • The algorithm significantly improves the accuracy of subsequent dynamic gesture recognition.
  • This method supports more natural and effective interactions within AR platforms.