Maqbool, HF orcid.org/0000-0003-3193-4984, Husman, MAB, Awad, MI orcid.org/0000-0002-0367-0187 et al. (3 more authors) (2017) Stance Sub-Phases Gait Event Detection in Real-time for Ramp Ascent and Descent. In: Ibáñez, J, González-Vargas, J, Azorín, JM, Akay, M and Pons, JL, (eds.) Converging Clinical and Engineering Research on Neurorehabilitation II. 3rd International Conference on NeuroRehabilitation (ICNR2016), 18-21 Oct 2016, Segovia, Spain. Springer International Publishing , pp. 191-196. ISBN 978-3-319-46668-2
Abstract
This article presents a real-time gait event/phase detection system for control subjects and lower limb amputees during ramp ascent (RA) and ramp descent (RD) using a single wearable sensor. Development of the algorithm is based on the shank angular velocity in the sagittal plane and linear acceleration signal in the shank longitudinal direction. System performance was evaluated with nine control subjects (CS) and one transfemoral amputee (TFA) and the results were validated with foot-switches. Results were promising for Initial-Contact (IC) and Toe-Off (TO) across all the subjects. Higher mean differences were found out for Foot-Flat start and Heel-Off, particularly in the case of TFA due to the difference in kinematics behavior compared to CS. Success detection rate of 99.7 % was achieved for RA and RD in both groups.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Keywords: | Gait Event Detection; Lower Limb Prosthesis; Stance Sub-Phases |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/K020463/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Jul 2016 10:43 |
Last Modified: | 16 Aug 2017 16:35 |
Status: | Published |
Publisher: | Springer International Publishing |
Identification Number: | 10.1007/978-3-319-46669-9_34 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:102337 |