Gaussian Process Regression for COP Trajectory Estimation in Healthy and Pathological Gait Using Instrumented Insoles

Ton T.H. Duong, David Uher, Sally Dunaway Young, Tina Duong, Monica Sangco, Kayla Cornett, Jacqueline Montes, Damiano Zanotto

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

9 Scopus citations

Abstract

Research in powered prostheses and orthoses has relied on COP measurements to inform a device's controller about the body's progression through the gait cycle, and to provide sensory substitution for prosthesis users, thereby helping them maintain balance during locomotion. Obtaining accurate COP measurements in out-of-the-lab contexts currently requires pressure sensitive insoles with dense arrays of sensing elements, which are expensive and bulky, limiting the accessibility and scalability of this technology. In this paper, we present a new method to reconstruct COP trajectories in over-ground walking tasks, using an affordable sensor array with eight sensing elements embedded in shoe insoles. The method leverages Gaussian Process Regression (GPR) to perform predictions from raw sensor data using Bayesian inference. A preliminary validation was carried out with a convenience sample of healthy individuals and patients with neuromuscular disorders. Combined mediolateral (ML) and anteroposterior (AP) errors where 2% and 3% for healthy individuals and patients, respectively. The analysis evidenced larger stride-to-stride variability in the ML COP excursion for the patient group, suggesting higher levels of motor noise associated with selective muscle weakness. These promising results indicate the potential of the proposed method to accurately estimate COP trajectories for future applications in wearable robotics and out-of-the-lab clinical gait assessments.

Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Pages9548-9553
Number of pages6
ISBN (Electronic)9781665417143
DOIs
StatePublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 - Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Country/TerritoryCzech Republic
CityPrague
Period27/09/211/10/21

Keywords

  • Ambulatory Gait Analysis
  • Instrumented Footwear
  • Machine Learning Inference Models
  • Wearable Technology

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