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Real-Time Arm Tracking for HMI Applications.

Matthew Masters, Luke Osborn, Nitish Thakor1

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218.

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks
|May 26, 2021
PubMed
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This study presents a low-cost, wearable limb tracking system using sensor fusion in inertial measurement units (IMUs). The system minimizes computational needs for human-machine interfaces (HMIs) with an average accuracy of 2.9°.

Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Sensor Technology

Background:

  • Limb tracking is crucial for human-machine interfaces (HMIs).
  • Existing systems often suffer from high computational costs, complex algorithms, and expensive hardware.
  • There is a need for accessible and efficient limb tracking solutions.

Purpose of the Study:

  • To develop a low-cost, wearable limb tracking system.
  • To reduce computational requirements for human-machine interfaces.
  • To improve the accuracy and usability of limb tracking technology.

Main Methods:

  • Utilized sensor fusion algorithms with commercial inertial measurement units (IMUs).
  • Combined accelerometer and gyroscope data to minimize processing power.

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  • Incorporated magnetometer data to mitigate sensor drift using quaternion-vector manipulations.
  • Main Results:

    • Achieved an average Root Mean Squared Error (RMSE) of approximately 2.9° for angle estimation.
    • Demonstrated a wearable, minimally complex, and low-cost limb tracking system.
    • Validated the system's effectiveness in various HMI applications.

    Conclusions:

    • The developed limb tracking system offers a practical and affordable solution for HMIs.
    • Sensor fusion in IMUs provides an efficient method for limb tracking.
    • The system's simplicity and low cost enhance its applicability in diverse human-machine interaction scenarios.