A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees

Journal article


Nsugbe, E., Samuel, O., Asogbon, M. and Jose, J. R. L. 2024. A Pilot on the use of Stride Cadence for the Charac-terization of Walking Ability in Lower Limb Ampu-tees. Biomedical Engineering Advances. 7 (2024), pp. 1-10. https://doi.org/10.1016/j.bea.2024.100117
AuthorsNsugbe, E., Samuel, O., Asogbon, M. and Jose, J. R. L.
Abstract

Amputations are a prominent affliction that occur worldwide, with causes ranging from congenital, dis-ease-based, or external reasons such as trauma. Prosthesis provides the closest alternative functional replace-ment to the loss of a limb. Before any form of rehabilitation support can be offered to amputee patients, an as-sessment of their degree and level of mobility first needs to be evaluated using the K-level grading system. The typical means towards the assigning of a K-level grading is through qualitative methods, which have been criti-cized for being subjective and, at times, imprecise. As a means towards remedying this shortcoming, we investi-gated the prospect of utilizing data from wearable sensors for analyzing the stride pattern and cadence of vari-ous subjects towards the quantitative inference of a K-level. This was accomplished using data from accelerom-eters, alongside advanced signal processing and machine learning models, towards the quantitative identifica-tion and differentiation of the various K-levels of amputees of varied levels of mobility. The experimental results showed that this aim could be accomplished under the circumstance investigated and the models applied as part of this research. Additional analysis was also done on the use of data from accelerometers towards the differen-tiation between amputated and non-amputated subjects, which showed that the cohorts could be classified and differentiated using purely accelerometer data and the accompanying postprocessing methods.

KeywordsLower limb; Signal processing; Machine learning; Artificial intelligence; LSDL; Prosthesis; Wearable sensors; Amputations; Biomechanics; Orthopedics
Year2024
JournalBiomedical Engineering Advances
Journal citation7 (2024), pp. 1-10
PublisherElsevier BV
ISSN2667-0992
Digital Object Identifier (DOI)https://doi.org/10.1016/j.bea.2024.100117
Web address (URL)https://doi.org/10.1016/j.bea.2024.100117
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Online20 Feb 2024
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Accepted19 Feb 2024
Deposited20 May 2024
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