Improving the accuracy of wearable sensors for human locomotion tracking using phase-locked regression models

Ton T.H. Duong, Huanghe Zhang, T. Sean Lynch, Damiano Zanotto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

The trend toward soft wearable robotic systems creates a compelling need for new and reliable sensor systems that do not require a rigid mounting frame. Despite the growing use of inertial measurement units (IMUs) in motion tracking applications, sensor drift and IMU-to-segment misalignment still represent major problems in applications requiring high accuracy. This paper proposes a novel 2-step calibration method which takes advantage of the periodic nature of human locomotion to improve the accuracy of wearable inertial sensors in measuring lower-limb joint angles. Specifically, the method was applied to the determination of the hip joint angles during walking tasks. The accuracy and precision of the calibration method were accessed in a group of N =8 subjects who walked with a custom-designed inertial motion capture system at 85% and 115% of their comfortable pace, using an optical motion capture system as reference. In light of its low computational complexity and good accuracy, the proposed approach shows promise for embedded applications, including closed-loop control of soft wearable robotic systems.

Original languageEnglish
Title of host publication2019 IEEE 16th International Conference on Rehabilitation Robotics, ICORR 2019
Pages145-150
Number of pages6
ISBN (Electronic)9781728127552
DOIs
StatePublished - Jun 2019
Event16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019 - Toronto, Canada
Duration: 24 Jun 201928 Jun 2019

Publication series

NameIEEE International Conference on Rehabilitation Robotics
Volume2019-June
ISSN (Print)1945-7898
ISSN (Electronic)1945-7901

Conference

Conference16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019
Country/TerritoryCanada
CityToronto
Period24/06/1928/06/19

Keywords

  • Human motion analysis
  • Inertial sensors
  • Wearable technology

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