A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees
Amputations are a prominent affliction that occur worldwide, with causes ranging from congenital, disease-based, or external reasons such as trauma. Prosthesis provides the closest alternative functional replacement to the loss of a limb. Before any form of rehabilitation support can be offered to a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Biomedical Engineering Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667099224000069 |
| _version_ | 1797299727322054656 |
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| author | Ejay Nsugbe Oluwarotimi Williams Samuel Mojisola Grace Asogbon Jose Javier Reyes-Lagos |
| author_facet | Ejay Nsugbe Oluwarotimi Williams Samuel Mojisola Grace Asogbon Jose Javier Reyes-Lagos |
| author_sort | Ejay Nsugbe |
| collection | DOAJ |
| description | Amputations are a prominent affliction that occur worldwide, with causes ranging from congenital, disease-based, or external reasons such as trauma. Prosthesis provides the closest alternative functional replacement to the loss of a limb. Before any form of rehabilitation support can be offered to amputee patients, an assessment 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 criticized for being subjective and, at times, imprecise. As a means towards remedying this shortcoming, we investigated the prospect of utilizing data from wearable sensors for analyzing the stride pattern and cadence of various subjects towards the quantitative inference of a K-level. This was accomplished using data from accelerometers, alongside advanced signal processing and machine learning models, towards the quantitative identification 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 differentiation 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. |
| first_indexed | 2024-03-07T22:53:54Z |
| format | Article |
| id | doaj.art-f979dce6c252403a8af05a63d3bd59db |
| institution | Directory Open Access Journal |
| issn | 2667-0992 |
| language | English |
| last_indexed | 2024-03-07T22:53:54Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biomedical Engineering Advances |
| spelling | doaj.art-f979dce6c252403a8af05a63d3bd59db2024-02-23T05:00:58ZengElsevierBiomedical Engineering Advances2667-09922024-06-017100117A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputeesEjay Nsugbe0Oluwarotimi Williams Samuel1Mojisola Grace Asogbon2Jose Javier Reyes-Lagos3Nsugbe Research Labs, Swindon, UK; Corresponding author.School of Computing and Engineering, University of Derby, Derby, UKShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, ChinaSchool of Medicine, Autonomous University of Mexico State (UAEMéx), Toluca de Lerdo, MexicoAmputations are a prominent affliction that occur worldwide, with causes ranging from congenital, disease-based, or external reasons such as trauma. Prosthesis provides the closest alternative functional replacement to the loss of a limb. Before any form of rehabilitation support can be offered to amputee patients, an assessment 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 criticized for being subjective and, at times, imprecise. As a means towards remedying this shortcoming, we investigated the prospect of utilizing data from wearable sensors for analyzing the stride pattern and cadence of various subjects towards the quantitative inference of a K-level. This was accomplished using data from accelerometers, alongside advanced signal processing and machine learning models, towards the quantitative identification 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 differentiation 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.http://www.sciencedirect.com/science/article/pii/S2667099224000069Lower limbSignal processingMachine learningArtificial intelligenceLSDLProsthesis |
| spellingShingle | Ejay Nsugbe Oluwarotimi Williams Samuel Mojisola Grace Asogbon Jose Javier Reyes-Lagos A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees Biomedical Engineering Advances Lower limb Signal processing Machine learning Artificial intelligence LSDL Prosthesis |
| title | A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees |
| title_full | A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees |
| title_fullStr | A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees |
| title_full_unstemmed | A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees |
| title_short | A pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees |
| title_sort | pilot on the use of stride cadence for the characterization of walking ability in lower limb amputees |
| topic | Lower limb Signal processing Machine learning Artificial intelligence LSDL Prosthesis |
| url | http://www.sciencedirect.com/science/article/pii/S2667099224000069 |
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