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

Tools

Meng, Lin and Li, Baihan and Childs, Craig and Buis, Arjan and He, Feng and Ming, Dong; (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, CHN, pp. 1-5. ISBN 9781538683446 (https://doi.org/10.1109/CIVEMSA45640.2019.9071595)

[thumbnail of Meng-etal-CIVEMSA-2019-Effect-of-walking-variations-on-complementary-filter-based-inertial-data-fusion-for-ankle-angle]
Preview
 Text. Filename: Meng_etal_CIVEMSA_2019_Effect_of_walking_variations_on_complementary_filter_based_inertial_data_fusion_for_ankle_angle.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (1MB)| Preview

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.

ORCID iDs

Meng, Lin, Li, Baihan, Childs, Craig ORCID logoORCID: https://orcid.org/0000-0003-1318-0007, Buis, Arjan ORCID logoORCID: https://orcid.org/0000-0003-3947-293X, He, Feng and Ming, Dong;
  • Item type:Book Section
    ID code:82377
    Dates:
    Date
    Event
    20 April 2020
    Published
    16 June 2019
    Published Online
    25 March 2019
    Accepted
    Notes:© 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.
    Subjects:Technology > Engineering (General). Civil engineering (General) > Bioengineering
    Department:Faculty of Engineering > Biomedical Engineering
    Strategic Research Themes > Health and Wellbeing
    Depositing user: Pure Administrator
    Date deposited:21 Sep 2022 08:19
    Last modified:12 Dec 2024 01:33
    URI:https://strathprints.strath.ac.uk/id/eprint/82377
CORE (COnnecting REpositories)