Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is...

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Vydáno v:IEEE sensors journal Ročník 20; číslo 14; s. 7969 - 7979
Hlavní autoři: Weygers, Ive, Kok, Manon, De Vroey, Henri, Verbeerst, Tommy, Versteyhe, Mark, Hallez, Hans, Claeys, Kurt
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acceleration
Accelerometers
Algorithms
Biomechanics
Body sensor networks
Drift
Filtration
Gait
Gyroscopes
Inertial platforms
Inertial sensing devices
inertial-sensor drift
Joints (anatomy)
Kinematics
Knee
motion analysis
Motion capture
Motion segmentation
Optimization
Reference systems
Robot arms
Robot sensing systems
Segments
sensor fusion
Sensors
Smoothing
wearable sensors
ISSN:1530-437X, 1558-1748
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Popis
Shrnutí:The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in drifting joint kinematics. This study proposes a novel joint kinematic estimation method that tightly incorporates the connection between adjacent segments within a sensor fusion algorithm, to obtain drift-free joint kinematics. Drift in the joint kinematics is eliminated solely by utilizing common information in the accelerometer and gyroscope measurements of sensors placed on connecting segments. Both an optimization-based smoothing and a filtering approach were implemented. Validity was assessed on a robotic manipulator under varying measurement durations and movement excitations. Standard deviations of the estimated relative sensor orientations were below 0.89° in an optimization-based smoothing implementation for all robot trials. The filtering implementation yielded similar results after convergence. The method is proven to be applicable in biomechanics, with a prolonged gait trial of 7 minutes on 11 healthy subjects. Three-dimensional knee joint angles were estimated, with mean RMS errors of 2.14°, 1.85°, 3.66° in an optimization-based smoothing implementation and mean RMS errors of 3.08°, 2.42°, 4.47° in a filtering implementation, with respect to a golden standard optical motion capture reference system.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.2982459