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

Ivan Vatolik; Gordon Hunter; Nicola Swann; Michael Everington; O. Lanets; S. Liaskova; C. Mbachu; Andy Augousti

doi:10.34961/researchrepository-ul.29290907.v1

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

Ivan Vatolik
, Gordon Hunter
, Nicola Swann
, Michael Everington
, O. Lanets
, S. Liaskova
, C. Mbachu
, Andy Augousti

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Number of pages	5
DOIs	https://doi.org/10.34961/researchrepository-ul.29290907.v1
Publication status	Published - 13 Aug 2024
Event	Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS) - Limerick, Ireland Duration: 11 Aug 2024 → 14 Aug 2024

Conference

Conference	Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS)
Period	11/08/24 → 14/08/24

Bibliographical note

Organising Body: Institute of Measurement Control

Keywords

Mechanical, aeronautical and manufacturing engineering

Access to Document

10.34961/researchrepository-ul.29290907.v1Licence: CC BY-NC-SA

Cite this

Vatolik, I., Hunter, G., Swann, N., Everington, M., Lanets, O., Liaskova, S., Mbachu, C., & Augousti, A. (2024). Use of deep learning techniques to classify acoustic emission data from knee joints. Paper presented at Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS). https://doi.org/10.34961/researchrepository-ul.29290907.v1

Vatolik, Ivan ; Hunter, Gordon ; Swann, Nicola et al. / Use of deep learning techniques to classify acoustic emission data from knee joints. Paper presented at Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS).5 p.

@conference{6886999849ed477fbca03e11ada36afe,

title = "Use of deep learning techniques to classify acoustic emission data from knee joints",

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. ",

keywords = "Mechanical, aeronautical and manufacturing engineering",

author = "Ivan Vatolik and Gordon Hunter and Nicola Swann and Michael Everington and O. Lanets and S. Liaskova and C. Mbachu and Andy Augousti",

note = "Organising Body: Institute of Measurement Control; Sensors and their applications 2024 : 20th Sensors \& their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS) ; Conference date: 11-08-2024 Through 14-08-2024",

year = "2024",

month = aug,

day = "13",

doi = "10.34961/researchrepository-ul.29290907.v1",

language = "English",

}

Vatolik, I, Hunter, G, Swann, N, Everington, M, Lanets, O, Liaskova, S, Mbachu, C & Augousti, A 2024, 'Use of deep learning techniques to classify acoustic emission data from knee joints', Paper presented at Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS), 11/08/24 - 14/08/24. https://doi.org/10.34961/researchrepository-ul.29290907.v1

Use of deep learning techniques to classify acoustic emission data from knee joints. / Vatolik, Ivan; Hunter, Gordon; Swann, Nicola et al.
2024. Paper presented at Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS).

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Use of deep learning techniques to classify acoustic emission data from knee joints

AU - Vatolik, Ivan

AU - Hunter, Gordon

AU - Swann, Nicola

AU - Everington, Michael

AU - Lanets, O.

AU - Liaskova, S.

AU - Mbachu, C.

AU - Augousti, Andy

N1 - Organising Body: Institute of Measurement Control

PY - 2024/8/13

Y1 - 2024/8/13

N2 - 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.

AB - 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.

KW - Mechanical, aeronautical and manufacturing engineering

U2 - 10.34961/researchrepository-ul.29290907.v1

DO - 10.34961/researchrepository-ul.29290907.v1

M3 - Paper

T2 - Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS)

Y2 - 11 August 2024 through 14 August 2024

ER -

Vatolik I, Hunter G, Swann N, Everington M, Lanets O, Liaskova S et al.. Use of deep learning techniques to classify acoustic emission data from knee joints. 2024. Paper presented at Sensors and their applications 2024 : 20th Sensors & their Applications Conference co-located with 5th International Conference of Fibre Optic and Photonic Sensors for Industrial and Safety Applications (OFSIS). doi: 10.34961/researchrepository-ul.29290907.v1

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

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this