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Title: Comparison of gait event detection from shanks and feet in single-task and multi-task walking of healthy older adults
Authors: Kong, Weisheng
Lin, J.
Waaning, Lauren
Sessa, Salvatore
Cosentino, Sarah
Magistro, Daniele
Zecca, Massimiliano
Kawashima, Ryuta
Takanishi, Atsuo
Keywords: Legged locomotion
Angular velocity
Integrated circuits
Foot
Event detection
Acceleration
Turning
Issue Date: 2017
Publisher: © IEEE
Citation: KONG, W. ... et al, 2017. Comparison of gait event detection from shanks and feet in single-task and multi-task walking of healthy older adults. Proceedings of the 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO 2016), Qingdao, China, 3rd-7th December 2017, pp. 2063-2068.
Abstract: Automatic and objective detection algorithms for gait events from MEMS Inertial Measurement Units data have been developed to overcome subjective inaccuracy in traditional visual observation. Their accuracy and sensitivity have been verified with healthy older adults, Parkinson's disease and spinal injured patients, using single-task gait exercises, where events are precise as the subject is focusing only on walking. Multi-task walking instead simulates a more realistic and challenging scenario where subjects perform secondary cognitive task while walking, so it is a better benchmark. In this paper, we test two algorithms based on shank and foot angular velocity data in single-task, dual-task and multi-task walking. Results show that both algorithms fail when the subject slows extremely down or pauses due to high cognitive and attentional load, and, in particular, the first stride detection error rate of the foot-based algorithm increases. Stride time is accurate with both algorithms regardless of walking types, but the shank-based algorithm leads to an overestimation on the proportion of swing phase in one gait cycle. Increasing the number of cognitive tasks also causes this error with both algorithms.
Description: © 2016 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.
Sponsor: This research has been supported by the JSPS Grant-in-Aid for Young Scientists (Wakate B) [15K21437], FY2016 Grant Program for Promotion of International Joint Research of Waseda University, and also in part by the Program for Leading Graduate Schools, Graduate Program for Embodiment Informatics of the Ministry of Education, Culture, Sports, Science and Technology.
Version: Accepted for publication
DOI: 10.1109/ROBIO.2016.7866633
URI: https://dspace.lboro.ac.uk/2134/25486
Publisher Link: http://dx.doi.org/10.1109/ROBIO.2016.7866633
ISBN: 9781509043644
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)
Conference Papers and Presentations (Sport, Exercise and Health Sciences)

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