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Updated: Jun 27, 2026
06:52
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
Published on: May 26, 2020
A Tightly Coupled Multibody Dynamics and Multi-Sensor Fusion Algorithm for Simultaneous Kinematics and Kinetics
Hassan Osman1, Daan de Kanter1, Jelle Boelens1,2
1Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces a new method for accurate motion capture using Inertial Measurement Units (IMUs) by integrating sensor data with dynamic models. The approach enhances joint kinematics and kinetics estimation for mobility disorder diagnosis and rehabilitation.
Area of Science:
- Biomechanics
- Robotics
- Sensor Fusion
Background:
- Inertial Measurement Units (IMUs) offer portable motion capture but face challenges like magnetic distortion and integration drift.
- Accurate estimation of kinematics and kinetics is crucial for diagnosing mobility disorders and guiding rehabilitation.
Purpose of the Study:
- To develop a tightly coupled motion-capture approach integrating IMU data with multibody dynamic models.
- To simultaneously estimate system kinematics and kinetics using only accelerometer and gyroscope data.
Main Methods:
- Implemented an iterated extended Kalman filter to fuse IMU measurements with multibody dynamic models.
- Enforced complete multibody system dynamics to improve estimation accuracy.
- Validated the approach using pendulum and collaborative robot systems with ground-truth data.
Main Results:
- Achieved a maximum joint angle root-mean-square difference of 3.75° for a pendulum and 3.24° for a robot.
- Demonstrated accurate kinetic estimates with a maximum joint torque root-mean-square difference of 3.02 Nm (pendulum) and 4.27 Nm (robot).
- Showcased the potential for fusing additional sensor data to further enhance accuracy.
Conclusions:
- The proposed method accurately estimates joint kinematics and kinetics from IMU data, overcoming common sensor limitations.
- This approach holds promise for reliable motion analysis in clinical and home-based rehabilitation settings.
- The algorithm's flexibility allows for fusion with other sensor types for improved performance.
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