Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (9), p.2706
Main Authors: Koshio, Tatsuki, Haraguchi, Naoto, Takahashi, Takayoshi, Hara, Yuse, Hase, Kazunori
Format: Article
Language:English
Subjects:
Acceleration
Adult
Algorithms
Analysis
biomechanical analysis
Biomechanical Phenomena - physiology
Biomechanics
contact model
ground reaction moment
human model
Humans
Kalman filter
Kinematics
Machine learning
Male
Measurement
Methods
Movement - physiology
optimization
Pelvis
Sensors
Simulation
Skating
Sports - physiology
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Summary:Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24092706