In-vitro validation of inertial-sensor-to-bone alignment

A major shortcoming in kinematic estimation using skin-attached inertial sensors is the alignment of sensor-embedded and segment-embedded coordinate systems. Only a correct alignment results in clinically relevant kinematics. Model-based inertial-sensor-to-bone alignment methods relate inertial sens...

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Bibliographic Details
Published in:Journal of biomechanics 2021-11, Vol.128, p.110781-110781, Article 110781
Main Authors: Weygers, Ive, Kok, Manon, Seel, Thomas, Shah, Darshan, Taylan, Orçun, Scheys, Lennart, Hallez, Hans, Claeys, Kurt
Format: Article
Language:English
Subjects:
Alignment
Axes (reference lines)
Biomechanical Phenomena
Biomechanics
Cadavers
Calibration
Coordinates
Disturbances
Human movement analysis
Humans
IMU
Inertial sensing devices
Joint kinematics
Joints (anatomy)
Kinematics
Lower limb
Motion capture
Movement
Range of Motion, Articular
Rotation
Segments
Sensor-to-segment alignment
Sensors
Soft tissues
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Summary:A major shortcoming in kinematic estimation using skin-attached inertial sensors is the alignment of sensor-embedded and segment-embedded coordinate systems. Only a correct alignment results in clinically relevant kinematics. Model-based inertial-sensor-to-bone alignment methods relate inertial sensor measurements with a model of the joint. Therefore, they do not rely on properly executed calibration movements or a correct sensor placement. However, it is unknown how accurate such model-based methods align the sensor axes and the underlying segment-embedded axes, as defined by clinical definitions. Also, validation of the alignment models is challenging, since an optical motion capture ground truth can be prone to disturbances from soft tissue movement, orientation estimation and manual palpation errors. We present an anatomical tibiofemoral ground truth on an unloaded cadaveric measurement set-up that intrinsically overcomes these disturbances. Additionally, we validate existing model-based alignment strategies. Modeling the degrees of freedom leads to the identification of rotation axes. However, there is no reason why these axes would align with the segment-embedded axes. Relative inertial-sensor orientation information and rich arbitrary movements showed to aid in identifying the underlying joint axes. The first dominant sagittal rotation axis aligned sufficiently well with the underlying segment-embedded reference. The estimated axes that relate to secondary kinematics tend to deviate from the underlying segment-embedded axes as much as their expected range of motion around the axes. In order to interpret the secondary kinematics, the alignment model should more closely match the biomechanics of the joint.
ISSN:0021-9290
1873-2380
DOI:10.1016/j.jbiomech.2021.110781