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Use of deep learning techniques to classify acoustic emission data from knee joints

Date
2024
Abstract
We report here on the application of deep learning techniques to distinguish acoustic emission signals from human knees, recorded on 30 volunteers divided into three age groups 18-34, 35-49 and 50+. The deep learning model developed and applied to this data was able to correctly identify samples from each category with success rates of up to 89.5% in some cases, an outcome that is much higher than would be expected from random selection.
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Publisher
University of Limerick
Citation
20th Sensors and Their Applications Conference, 2024, Paper No: 43
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20th Sensors & Their Applications Conference
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Paper_43.pdf
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Keywords
acoustic knee emissions, deep learning, Engineering
ULRR Identifiers
https://doi.org/10.34961/researchrepository-ul.29290907
 https://researchrepository.ul.ie/handle/10344/14808
Funding code
Funding Information
Sustainable Development Goals
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Type
Meetings and Proceedings
Rights
https://creativecommons.org/licenses/by-nc-sa/4.0/
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