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

Howard Chen; Mark C Schall; Nathan B Fethke

doi:10.1016/j.apergo.2020.103187

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

Journal article

Open access

Peer reviewed

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

Howard Chen, Mark C Schall and Nathan B Fethke

Applied ergonomics, Vol.89, pp.103187-103187

11/2020

DOI: 10.1016/j.apergo.2020.103187

PMID: 32854821

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https://www.ncbi.nlm.nih.gov/pmc/articles/9605636View

Open Access

Abstract

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 <4.5° with a computationally efficient, non-proprietary complementary filter that did not account for gyroscope bias during each of the one-minute trials. Controlling for gyroscope bias reduced peak displacement errors to <3.0°. The complementary filters were comparable (<1° peak displacement difference) to the more complex Kalman filters. •Principles of sensor fusion algorithms for inertial measurement units are reviewed.•A computationally efficient complementary filter is presented.•Accuracy of the filter was comparable to a more complex Kalman filter.•Implications for occupational exposure assessment are discussed.

Complementary filter

Inclinometer

Inertial measurement units

Kalman filter

Inertial-based motion capture

Details

Title: Subtitle: Measuring upper arm elevation using an inertial measurement unit: An exploration of sensor fusion algorithms and gyroscope models
Creators: Howard Chen - Department of Mechanical Engineering, Auburn University, AL, USA
Mark C Schall - Department of Industrial and Systems Engineering, Auburn University, AL, USA
Nathan B Fethke - Department of Occupational and Environmental Health, University of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: Applied ergonomics, Vol.89, pp.103187-103187
DOI: 10.1016/j.apergo.2020.103187
PMID: 32854821
NLM abbreviation: Appl Ergon
ISSN: 0003-6870
eISSN: 1872-9126
Publisher: Elsevier Ltd
Grant note: DOI: 10.13039/100000125, name: Centers for Disease Control (CDC)/National Institute for Occupational Safety and Health; DOI: 10.13039/100008893, name: University of Iowa, award: T42OH008491; DOI: 10.13039/100008333, name: University of Alabama-Birmingham; DOI: 10.13039/100013080, name: Auburn University, award: T42OH008436; name: Mentored Research Scientist Development, award: K01OH011183
Language: English
Date published: 11/2020
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Occupational and Environmental Health; Industrial and Systems Engineering; Injury Prevention Research Center
Record Identifier: 9984066104402771

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