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Novel magnetometer-free inertial-measurement-unit-based orientation estimation approach for measuring upper limb
Souha Baklouti1,2, Taysir Rezgui3, Abdelbadia Chaker1,4
1Mechanical Laboratory of Sousse (LMS), National School of Engineers of Sousse, University of Sousse, Sousse, Tunisia.
Wearable Technologies
|July 22, 2025
Summary
This study introduces a magnetometer-free Kalman filter for accurate human joint orientation estimation using wearable sensors. The new method improves accuracy in environments with magnetic disturbances, outperforming existing filters.
Area of Science:
- Biomedical Engineering
- Robotics
- Sensor Fusion
Background:
- Accurate joint orientation estimation is crucial for human movement analysis.
- Wearable inertial measurement units (IMUs) are widely used but face challenges from magnetic disturbances and motion variability.
- Existing sensor fusion algorithms often rely on magnetometers, which are susceptible to environmental interference.
Purpose of the Study:
- To develop and validate a magnetometer-free Kalman filter (KF) approach for robust joint orientation estimation.
- To address limitations of current methods in environments with magnetic distortions and complex human motion.
- To improve the accuracy and reliability of orientation estimation using wearable IMUs.
Main Methods:
- A refined Kalman filter (KF) framework was developed, analyzing accelerometer alignment with the Earth's frame to estimate orientation and correct gyroscope data.
- The proposed algorithm eliminates the need for magnetometer inputs, mitigating susceptibility to magnetic disturbances.
- The approach was tested using controlled robotic movements and real-world upper-limb motion monitoring, with comparative analysis against double-stage Kalman filter (DSKF) and complementary filters.
Main Results:
- The magnetometer-free KF approach demonstrated superior performance in orientation estimation, particularly for yaw measurements, compared to DSKF and complementary filters.
- Achieved significantly lower root mean square error (RMSE) and mean absolute error (MAE) in robotic movement analysis.
- Validation against a motion capture system showed error metrics within acceptable ranges ( of joint ROM) with strong correlation coefficients (), though some deviations occurred during complex movements.
Conclusions:
- The proposed magnetometer-free KF approach offers a promising and accurate alternative for human joint orientation estimation.
- It effectively overcomes challenges posed by magnetic distortions in industrial and other environments.
- Further refinement is suggested for complex motion intervals to enhance overall robustness.
