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A fast Gauss-Newton optimizer for estimating human body orientation
1Department of Mechanical Engineering, University of Victoria, BC, V8W 3P6, Canada. jklee@me.uvic.ca
Summary
This study introduces a new quaternion-based Gauss-Newton optimizer for tracking human body orientation. The efficient algorithm uses virtual rotations and situational measurement selection for robust, real-time performance with inertial/magnetic sensors.
Area of Science:
- Biomedical Engineering
- Robotics
- Sensor Fusion
Background:
- Accurate human body orientation tracking is crucial for real-time ambulatory applications.
- Existing methods often struggle with computational efficiency and robustness against motion artifacts and magnetic disturbances.
- Low-cost, real-time solutions are needed for widespread adoption.
Purpose of the Study:
- To develop a computationally efficient and robust quaternion-based Gauss-Newton optimizer for human body orientation tracking.
- To improve the performance of orientation estimation algorithms using inertial and magnetic sensors.
- To address challenges posed by fast body motions and temporary ferromagnetic interference.
Main Methods:
- Formulation of a quaternion-based Gauss-Newton optimizer.
- Implementation of a virtual rotation concept to reduce computational time.
- Adoption of a situational measurement vector selection procedure to enhance robustness.
Main Results:
- The proposed optimizer demonstrates computational efficiency.
- The algorithm shows robustness against fast body motions.
- Effective mitigation of temporary ferromagnetic disturbances was achieved.
Conclusions:
- The quaternion-based Gauss-Newton optimizer offers an efficient and robust solution for human body orientation tracking.
- Virtual rotations and situational measurement selection are key to the algorithm's performance.
- This approach is suitable for low-cost, real-time ambulatory applications.
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