Measuring upper arm elevation using an inertial measurement unit: An exploration of sensor fusion algorithms and gyroscope models

Many sensor fusion algorithms for analyzing human motion information collected with inertial measurement units have been reported in the scientific literature. Selecting which algorithm to use can be a challenge for ergonomists that may be unfamiliar with the strengths and limitations of the various...

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Bibliographic Details
Published in:Applied ergonomics 2020-11, Vol.89, p.103187-103187, Article 103187
Main Authors: Chen, Howard, Schall, Mark C., Fethke, Nathan B.
Format: Article
Language:English
Subjects:
Accelerometry - methods
Algorithms
Arm - physiology
Biomechanical Phenomena
Complementary filter
Humans
Inclinometer
Inertial measurement units
Inertial-based motion capture
Kalman filter
Models, Statistical
Motion
Occupational Exposure - analysis
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Summary:Many sensor fusion algorithms for analyzing human motion information collected with inertial measurement units have been reported in the scientific literature. Selecting which algorithm to use can be a challenge for ergonomists that may be unfamiliar with the strengths and limitations of the various options. In this paper, we describe fundamental differences among several algorithms, including differences in sensor fusion approach (e.g., complementary filter vs. Kalman Filter) and gyroscope error modeling (i.e., inclusion or exclusion of gyroscope bias). We then compare different sensor fusion algorithms considering the fundamentals discussed using laboratory-based measurements of upper arm elevation collected under three motion speeds. Results indicate peak displacement errors of
ISSN:0003-6870
1872-9126
1872-9126
DOI:10.1016/j.apergo.2020.103187