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Robust Orientation Estimation from MEMS Magnetic, Angular Rate, and Gravity (MARG) Modules for Human-Computer

Pontakorn Sonchan1, Neeranut Ratchatanantakit1, Nonnarit O-Larnnithipong1

  • 1Electrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USA.

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Summary
This summary is machine-generated.

A new algorithm, GMVDμK (GMVDK), enhances orientation estimation using Micro Electro-Mechanical Systems (MEMS) sensor modules. It offers improved robustness against magnetic disturbances for applications like human-computer interaction.

Keywords:
GMVDK algorithmGMVDμK algorithmMEMS MARG orientationmagnetic distortionorientation for human–computer interaction

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Area of Science:

  • Sensor Fusion and Inertial Navigation
  • Robotics and Human-Computer Interaction
  • Signal Processing for MEMS Devices

Background:

  • Low-cost Micro Electro-Mechanical Systems (MEMS) accelerometers, gyroscopes, and magnetometers offer potential for object tracking but suffer from signal quality limitations.
  • Existing MEMS Inertial Measurement Units (IMUs) and Magnetic, Angular Rate, and Gravity (MARG) sensor modules face challenges in long-term orientation estimation due to sensor noise and bias instability.
  • Accurate orientation tracking is crucial for applications such as human-computer interaction and Internet-Of-Things (IoT) device monitoring.

Purpose of the Study:

  • To develop and present a novel algorithm, GMVDμK (GMVDK), for robust orientation estimation using MARG sensor modules.
  • To leverage all available sensor signals from MARG modules to overcome limitations of individual sensors.
  • To specifically address the challenges of orientation estimation in human-computer interaction contexts.

Main Methods:

  • Development of the GMVDμK (GMVDK) algorithm, designed to utilize all signals from a MARG module.
  • Implementation of strategies to prevent overcorrection and enhance robustness against sensor noise and disturbances.
  • Experimental validation and comparison against existing MARG orientation estimation algorithms.

Main Results:

  • The GMVDμK (GMVDK) algorithm demonstrates robust orientation estimation by effectively utilizing all MARG sensor signals.
  • Experimental results show GMVDK significantly outperforms other algorithms in handling magnetic disturbances.
  • The algorithm successfully mitigates issues related to poor signal quality from low-cost MEMS sensors.

Conclusions:

  • The GMVDμK (GMVDK) algorithm represents a significant advancement in orientation estimation using low-cost MARG sensor modules.
  • GMVDK offers a more reliable solution for applications requiring accurate long-term orientation tracking, particularly in environments with magnetic interference.
  • This algorithm enhances the feasibility of using MEMS-based MARG sensors for advanced human-computer interaction and IoT applications.