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Real-Time Identification of Impaired Gait Phases Using a Single Foot-Mounted Inertial Sensor: Review and Feasibility Study

Lookup NU author(s): Professor Hermano Krebs

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Abstract

© 2018 IEEE. Contextualization: Identification of gait events is a key step towards enhancing control of robotic devices for rehabilitation and/or assistance of lower limb function. -Gap: Several approaches and techniques that can properly identify gait events and phases have been developed and successfully tested in healthy subjects, but current algorithms had limited success for impaired gait. -Purpose: Here we studied the feasibility of real-time identification of gait phases for impaired subjects using a single inertial sensor on the paretic foot. - Methodology: We carried out a pilot experiment evaluating seven algorithms proposed in the literature for detection of two main events (heel-strike [HS] and toe-off [TO]) for normal and hemiparetic gait. -Results: We obtained a high performance for healthy gait indicating their suitability for real-time implementation for that population. However, we obtained a lower accuracy for all the algorithms during hemiparetic-like gait. -Conclusions: We performed a comprehensive review of the literature and evaluated the current algorithms in gait segmentation using a single inertial sensor on foot or shank for both healthy and paretic gait. Existing algorithms worked as expected in healthy but not paretic gait.


Publication metadata

Author(s): Perez-Ibarra JC, Williams H, Siqueira AAG, Krebs HI

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

Year of Conference: 2018

Pages: 1157-1162

Online publication date: 11/10/2018

Acceptance date: 02/04/2018

ISSN: 2155-1782

Publisher: IEEE

URL: https://doi.org/10.1109/BIOROB.2018.8487694

DOI: 10.1109/BIOROB.2018.8487694

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538681831


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