Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement

Lin Meng; Baihan Li; Craig Childs; Arjan Buis; Feng He; Dong Ming

doi:10.1109/CIVEMSA45640.2019.9071595

Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement

Lin Meng, Baihan Li, Craig Childs, Arjan Buis, Feng He, Dong Ming

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

7 Downloads (Pure)

Abstract

A key problem on the measurement of lower-limb joint angles using inertial sensors is drift resulted in error accumulation after time integration. Several types of methods have been proposed to eliminate the drift. Among these methods, complementary filter-based sensor fusion algorithms are widely used in real-time applications due to its efficiency. Results from existing studies have shown that the performance of methods is relevant to walking speed. However, factors of walking variation have not been explored. This study first systematically investigated the walking variation factors and their effects on the accuracy of a proposed sensor fusion method during treadmill walking. Ten able-bodied participants participated in the experiment and walked on a treadmill with three different speeds (0.5, 1.0 and 1.5 m/s). A 12 camera Vicon motion capture system was used as the reference. The accuracy of the proposed method was evaluated in terms of the root-mean-square errors (RMSE), offsets and Pearson's correlation coefficients (PCC) in phases of a normalised gait cycle. A general linear model of analysis of variance (ANOVA) was used to analyze the factors including treadmill speed and gait phases. Results showed both factors had a significant influence on the RMSE, and only the treadmill speed had a significant influence on the offset. It provides an insight to improve the complementary filter-based method in future work.

Original language	English
Title of host publication	2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	9781538683446
ISBN (Print)	9781538683453
DOIs	https://doi.org/10.1109/CIVEMSA45640.2019.9071595
Publication status	Published - 20 Apr 2020
Event	IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications - Crowne Plaza, Tianjin, Tianjin, Jinnan, China Duration: 14 Jun 2019 → 16 Jun 2019 http://civemsa2019.ieee-ims.org/

Publication series

Name	Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)
Publisher	IEEE
ISSN (Print)	2377-9314
ISSN (Electronic)	2377-9322

Conference

Conference	IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications
Abbreviated title	CIVEMSA
Country/Territory	China
City	Tianjin, Jinnan
Period	14/06/19 → 16/06/19
Internet address	http://civemsa2019.ieee-ims.org/

Keywords

gait analysis
data fusion
complimentary filter
ankle angle measurement
treadmill speed
gait phases
sensors
foot
legged locomotion
angular velocity
knee
data integration
analysis of variance

Access to Document

10.1109/CIVEMSA45640.2019.9071595

Meng-etal-CIVEMSA-2019-Effect-of-walking-variations-on-complementary-filter-based-inertial-data-fusion-for-ankle-angleAccepted author manuscript, 1.63 MBLicence: Strath_1

Cite this

Meng, L., Li, B., Childs, C., Buis, A., He, F., & Ming, D. (2020). Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement. In 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (pp. 1-5). (Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)). IEEE. https://doi.org/10.1109/CIVEMSA45640.2019.9071595

Meng, Lin ; Li, Baihan ; Childs, Craig et al. / Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement. 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). Piscataway, NJ : IEEE, 2020. pp. 1-5 (Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)).

@inproceedings{1655baa286c74de8b610b0b10e260ed9,

title = "Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement",

abstract = "A key problem on the measurement of lower-limb joint angles using inertial sensors is drift resulted in error accumulation after time integration. Several types of methods have been proposed to eliminate the drift. Among these methods, complementary filter-based sensor fusion algorithms are widely used in real-time applications due to its efficiency. Results from existing studies have shown that the performance of methods is relevant to walking speed. However, factors of walking variation have not been explored. This study first systematically investigated the walking variation factors and their effects on the accuracy of a proposed sensor fusion method during treadmill walking. Ten able-bodied participants participated in the experiment and walked on a treadmill with three different speeds (0.5, 1.0 and 1.5 m/s). A 12 camera Vicon motion capture system was used as the reference. The accuracy of the proposed method was evaluated in terms of the root-mean-square errors (RMSE), offsets and Pearson's correlation coefficients (PCC) in phases of a normalised gait cycle. A general linear model of analysis of variance (ANOVA) was used to analyze the factors including treadmill speed and gait phases. Results showed both factors had a significant influence on the RMSE, and only the treadmill speed had a significant influence on the offset. It provides an insight to improve the complementary filter-based method in future work.",

keywords = "gait analysis, data fusion, complimentary filter, ankle angle measurement, treadmill speed, gait phases, sensors, foot, legged locomotion, angular velocity, knee, data integration, analysis of variance",

author = "Lin Meng and Baihan Li and Craig Childs and Arjan Buis and Feng He and Dong Ming",

note = "{\textcopyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA ; Conference date: 14-06-2019 Through 16-06-2019",

year = "2020",

month = apr,

day = "20",

doi = "10.1109/CIVEMSA45640.2019.9071595",

language = "English",

isbn = "9781538683453",

series = "Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)",

publisher = "IEEE",

pages = "1--5",

booktitle = "2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)",

url = "http://civemsa2019.ieee-ims.org/",

}

Meng, L, Li, B, Childs, C , Buis, A, He, F & Ming, D 2020, Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement. in 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), IEEE, Piscataway, NJ, pp. 1-5, IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Tianjin, Jinnan, China, 14/06/19. https://doi.org/10.1109/CIVEMSA45640.2019.9071595

Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement. / Meng, Lin; Li, Baihan; Childs, Craig et al.

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). Piscataway, NJ : IEEE, 2020. p. 1-5 (Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement

AU - Meng, Lin

AU - Li, Baihan

AU - Childs, Craig

AU - Buis, Arjan

AU - He, Feng

AU - Ming, Dong

N1 - © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2020/4/20

Y1 - 2020/4/20

N2 - A key problem on the measurement of lower-limb joint angles using inertial sensors is drift resulted in error accumulation after time integration. Several types of methods have been proposed to eliminate the drift. Among these methods, complementary filter-based sensor fusion algorithms are widely used in real-time applications due to its efficiency. Results from existing studies have shown that the performance of methods is relevant to walking speed. However, factors of walking variation have not been explored. This study first systematically investigated the walking variation factors and their effects on the accuracy of a proposed sensor fusion method during treadmill walking. Ten able-bodied participants participated in the experiment and walked on a treadmill with three different speeds (0.5, 1.0 and 1.5 m/s). A 12 camera Vicon motion capture system was used as the reference. The accuracy of the proposed method was evaluated in terms of the root-mean-square errors (RMSE), offsets and Pearson's correlation coefficients (PCC) in phases of a normalised gait cycle. A general linear model of analysis of variance (ANOVA) was used to analyze the factors including treadmill speed and gait phases. Results showed both factors had a significant influence on the RMSE, and only the treadmill speed had a significant influence on the offset. It provides an insight to improve the complementary filter-based method in future work.

AB - A key problem on the measurement of lower-limb joint angles using inertial sensors is drift resulted in error accumulation after time integration. Several types of methods have been proposed to eliminate the drift. Among these methods, complementary filter-based sensor fusion algorithms are widely used in real-time applications due to its efficiency. Results from existing studies have shown that the performance of methods is relevant to walking speed. However, factors of walking variation have not been explored. This study first systematically investigated the walking variation factors and their effects on the accuracy of a proposed sensor fusion method during treadmill walking. Ten able-bodied participants participated in the experiment and walked on a treadmill with three different speeds (0.5, 1.0 and 1.5 m/s). A 12 camera Vicon motion capture system was used as the reference. The accuracy of the proposed method was evaluated in terms of the root-mean-square errors (RMSE), offsets and Pearson's correlation coefficients (PCC) in phases of a normalised gait cycle. A general linear model of analysis of variance (ANOVA) was used to analyze the factors including treadmill speed and gait phases. Results showed both factors had a significant influence on the RMSE, and only the treadmill speed had a significant influence on the offset. It provides an insight to improve the complementary filter-based method in future work.

KW - gait analysis

KW - data fusion

KW - complimentary filter

KW - ankle angle measurement

KW - treadmill speed

KW - gait phases

KW - sensors

KW - foot

KW - legged locomotion

KW - angular velocity

KW - knee

KW - data integration

KW - analysis of variance

U2 - 10.1109/CIVEMSA45640.2019.9071595

DO - 10.1109/CIVEMSA45640.2019.9071595

M3 - Conference contribution book

SN - 9781538683453

T3 - Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

SP - 1

EP - 5

BT - 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

PB - IEEE

CY - Piscataway, NJ

T2 - IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

Y2 - 14 June 2019 through 16 June 2019

ER -

Meng L, Li B, Childs C , Buis A, He F, Ming D. Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement. In 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). Piscataway, NJ: IEEE. 2020. p. 1-5. (Proceedings of the IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)). Epub 2019 Jun 16. doi: 10.1109/CIVEMSA45640.2019.9071595

Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this