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dc.contributor.authorShahabpoor, E
dc.contributor.authorBrownjohn, JMW
dc.contributor.authorBillings, SA
dc.contributor.authorGuo, L-Z
dc.contributor.authorBocian, M
dc.date.accessioned2018-04-25T12:03:03Z
dc.date.issued2018-05-03
dc.description.abstractMonitoring natural human gait in real-life environment is essential in many applications including quantification of disease progression, and monitoring the effects of treatment and alteration of performance biomarkers in professional sports. Nevertheless, reliable and practical techniques and technologies necessary for continuous real-life monitoring of gait is still not available. This paper explores in detail the correlations between the acceleration of different body segments and walking ground reaction forces GRF(t) in three dimensions and proposes three sensory systems, with one, two and three inertial measurement units (IMUs), to estimate GRF(t) in the vertical (V), medial-lateral (ML) and anterior-posterior (AP) directions. The NARMAX non-linear system identification method was utilized to identify the optimal location for IMUs on the body for each system. A simple linear model was then proposed to estimate GRF(t) based on the correlation of segmental accelerations with each other. It was found that, for the three-IMU system, the proposed model estimated GRF(t) with average peak-to-peak normalized root mean square error (NRMSE) of 7%, 16% and 18% in V, AP and ML directions, respectively. With a simple subject-specific training at the beginning, these errors were reduced to 7%, 13% and 13% in V, AP and ML directions, respectively. These results were found favorably comparable with the results of the benchmark NARMAX model, with subject-specific training, with 0% (V), 4% (AP) and 1% (ML) NRMSE difference.en_GB
dc.description.sponsorshipThe authors acknowledge the financial support provided by the UK Engineering and Physical Sciences Research Council (EPSRC) for the following research grants: - Frontier Engineering Grant EP/K03877X/1 (Modelling complex and partially identified engineering problems: Application to the individualized multi-scale simulation of the musculoskeletal system); - Platform Grant EP/G061130/2 (Dynamic performance of large civil engineering structures: an integrated approach to management, design and assessment); and - Great Technologies Capital Call, Robotics and Autonomous Systems EP/J013714/1 (Human-Machine Co-operation in Robotics and Autonomous Systems).en_GB
dc.identifier.citationPublished online 03 May 2018.en_GB
dc.identifier.doi10.1109/TNSRE.2018.2830976
dc.identifier.urihttp://hdl.handle.net/10871/32605
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rights© The Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
dc.subjectambulationen_GB
dc.subjectbiomechanicsen_GB
dc.subjectblack-box approachen_GB
dc.subjectgait monitoringen_GB
dc.subjectoutdoor measurementen_GB
dc.titleReal-life Measurement of Tri-axial Walking Ground Reaction Forces using Optimal Network of Wearable Inertial Measurement Unitsen_GB
dc.typeArticleen_GB
dc.identifier.issn1534-4320
pubs.declined2018-04-20T12:22:39.689+0100
dc.descriptionThis is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.en_GB
dc.identifier.journalIEEE Transactions on Neural Systems and Rehabilitation Engineeringen_GB


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