King's College London

Research portal

Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Standard

Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals. / Malvuccio, Carlotta; Kamavuako, Ernest.

Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020. IEEE, 2021. p. 245-250 9398821 (Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020).

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Harvard

Malvuccio, C & Kamavuako, E 2021, Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals. in Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020., 9398821, Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020, IEEE, pp. 245-250. https://doi.org/10.1109/IECBES48179.2021.9398821

APA

Malvuccio, C., & Kamavuako, E. (2021). Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals. In Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020 (pp. 245-250). [9398821] (Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020). IEEE. https://doi.org/10.1109/IECBES48179.2021.9398821

Vancouver

Malvuccio C, Kamavuako E. Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals. In Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020. IEEE. 2021. p. 245-250. 9398821. (Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020). https://doi.org/10.1109/IECBES48179.2021.9398821

Author

Malvuccio, Carlotta ; Kamavuako, Ernest. / Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals. Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020. IEEE, 2021. pp. 245-250 (Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020).

Bibtex Download

@inbook{8c2de8adbfb3445187733fa0b5ded40a,
title = "Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals",
abstract = "Dehydration is a widespread problem in older adults. For this age group, dehydration can be fatal, however, there is a lack of accurate and valid methods to assess the level of hydration in healthcare settings. In this study, the use of surface electro-myographic (sEMG) sensors to detect active swallowing events (saliva vs liquid swallows) and to estimate fluid intake volume is investigated to corroborate the possibility of integrating these in a fluid intake tracking device. Eleven subjects (28\pm 13 years of age) were recruited, and sEMG recordings of single and continuous swallows from cups, bottles, and straws were analysed and four features were extracted. Results showed that the mean detection accuracy between noise and swallows was 99\pm1.31\% using a Fine Gaussian SVM classifier; and the mean classification accuracy between saliva and liquid swallows was 86.69\pm5.52\% using Fine KNN. Fluid volume was estimated using a feedforward artificial neural network, and results for single swallows presented a low mean RMSE value (2.01\pm 1.39\ \text{ml}), however the RMSE increased significantly for the estimation of continuous swallows (25.82\pm 26.39\ \ \text{ml}). These results suggest that while further validation is needed, especially in regards to volume estimation, the use of surface EMGs to track fluid intake seems promising.",
keywords = "dehydration , surface EMG , swallowing signals , saliva vs liquid , volume estimation, Support vector machines , Liquids , Volume measurement , Estimation , Sensor phenomena and characterization , Feature extraction , Electromyography",
author = "Carlotta Malvuccio and Ernest Kamavuako",
year = "2021",
month = apr,
day = "14",
doi = "10.1109/IECBES48179.2021.9398821",
language = "English",
isbn = "978-1-7281-4246-3",
series = "Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020",
publisher = "IEEE",
pages = "245--250",
booktitle = "Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020",

}

RIS (suitable for import to EndNote) Download

TY - CHAP

T1 - Detection of Swallowing Events and Fluid Intake Volume Estimation From Surface Electromyography Signals

AU - Malvuccio, Carlotta

AU - Kamavuako, Ernest

PY - 2021/4/14

Y1 - 2021/4/14

N2 - Dehydration is a widespread problem in older adults. For this age group, dehydration can be fatal, however, there is a lack of accurate and valid methods to assess the level of hydration in healthcare settings. In this study, the use of surface electro-myographic (sEMG) sensors to detect active swallowing events (saliva vs liquid swallows) and to estimate fluid intake volume is investigated to corroborate the possibility of integrating these in a fluid intake tracking device. Eleven subjects (28\pm 13 years of age) were recruited, and sEMG recordings of single and continuous swallows from cups, bottles, and straws were analysed and four features were extracted. Results showed that the mean detection accuracy between noise and swallows was 99\pm1.31\% using a Fine Gaussian SVM classifier; and the mean classification accuracy between saliva and liquid swallows was 86.69\pm5.52\% using Fine KNN. Fluid volume was estimated using a feedforward artificial neural network, and results for single swallows presented a low mean RMSE value (2.01\pm 1.39\ \text{ml}), however the RMSE increased significantly for the estimation of continuous swallows (25.82\pm 26.39\ \ \text{ml}). These results suggest that while further validation is needed, especially in regards to volume estimation, the use of surface EMGs to track fluid intake seems promising.

AB - Dehydration is a widespread problem in older adults. For this age group, dehydration can be fatal, however, there is a lack of accurate and valid methods to assess the level of hydration in healthcare settings. In this study, the use of surface electro-myographic (sEMG) sensors to detect active swallowing events (saliva vs liquid swallows) and to estimate fluid intake volume is investigated to corroborate the possibility of integrating these in a fluid intake tracking device. Eleven subjects (28\pm 13 years of age) were recruited, and sEMG recordings of single and continuous swallows from cups, bottles, and straws were analysed and four features were extracted. Results showed that the mean detection accuracy between noise and swallows was 99\pm1.31\% using a Fine Gaussian SVM classifier; and the mean classification accuracy between saliva and liquid swallows was 86.69\pm5.52\% using Fine KNN. Fluid volume was estimated using a feedforward artificial neural network, and results for single swallows presented a low mean RMSE value (2.01\pm 1.39\ \text{ml}), however the RMSE increased significantly for the estimation of continuous swallows (25.82\pm 26.39\ \ \text{ml}). These results suggest that while further validation is needed, especially in regards to volume estimation, the use of surface EMGs to track fluid intake seems promising.

KW - dehydration , surface EMG , swallowing signals , saliva vs liquid , volume estimation

KW - Support vector machines , Liquids , Volume measurement , Estimation , Sensor phenomena and characterization , Feature extraction , Electromyography

UR - http://www.scopus.com/inward/record.url?scp=85104839671&partnerID=8YFLogxK

U2 - 10.1109/IECBES48179.2021.9398821

DO - 10.1109/IECBES48179.2021.9398821

M3 - Conference paper

SN - 978-1-7281-4246-3

T3 - Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020

SP - 245

EP - 250

BT - Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020

PB - IEEE

ER -

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454