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Hybrid Orientation Based Human Limbs Motion Tracking Method.

Grzegorz Glonek1, Adam Wojciechowski2

  • 1Institute of Information Technology, Faculty of Technical Physics, Information Technology and Applied Mathematics, Lodz University of Technology, 215 Wolczanska street, 90-924 Lodz, Poland. grzegorz@glonek.net.pl.

Sensors (Basel, Switzerland)
|December 14, 2017
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Summary

This study introduces an orientation-based data fusion method for human motion tracking using depth sensors and inertial measurement units (IMU). This approach enhances limb tracking accuracy by 18% compared to traditional position-based methods.

Keywords:
IMUMicrosoft Kinectdata fusiondepth sensormotion tracking

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Area of Science:

  • Human-computer interaction
  • Biomechanical analysis
  • Motion capture technology

Background:

  • Accurate human motion tracking is crucial for human-machine interaction and diagnosing movement disorders.
  • Current body pose estimation relies on accessible tracking devices, but robustness can be improved through data fusion.
  • Existing methods predominantly use position-based data fusion, with limitations in precision and accuracy.

Purpose of the Study:

  • To introduce and validate a novel orientation-based data fusion approach for human limb motion tracking.
  • To improve the precision and robustness of motion tracking by fusing data from depth sensors and inertial measurement units (IMU).
  • To overcome the limitations of current position-based fusion methods in human pose estimation.

Main Methods:

  • Developed a novel orientation-based data fusion method specifically for depth sensors (e.g., Microsoft Kinect) and IMUs.
  • Analyzed the working characteristics of depth sensors and IMUs to create a method for precise limb orientation data fusion.
  • Designed experiments to rigorously verify the accuracy and performance of the proposed orientation-based fusion method.

Main Results:

  • The proposed orientation-based data fusion method demonstrated superior precision in tracking human limb orientation.
  • Experimental results confirmed the method's ability to compensate for individual device imprecisions.
  • The novel approach achieved up to an 18% improvement in precision compared to dominant position-based tracking methods.

Conclusions:

  • Orientation-based data fusion offers a significant advancement over position-based methods for human motion tracking.
  • The developed method enhances the accuracy and reliability of limb tracking using readily available sensors like depth cameras and IMUs.
  • This research provides a more precise and robust solution for applications in human-machine interaction and motion diagnosis.