A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation
Dehydration is a common problem among older adults. It can seriously affect their health and wellbeing and sometimes leads to death, given the diminution of thirst sensation as we age. It is, therefore, essential to keep older adults properly hydrated by monitoring their fluid intake and estimating...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/21/8789 |
| _version_ | 1827765438243667968 |
|---|---|
| author | Iman Ismail Imran Khan Niazi Heidi Haavik Ernest N. Kamavuako |
| author_facet | Iman Ismail Imran Khan Niazi Heidi Haavik Ernest N. Kamavuako |
| author_sort | Iman Ismail |
| collection | DOAJ |
| description | Dehydration is a common problem among older adults. It can seriously affect their health and wellbeing and sometimes leads to death, given the diminution of thirst sensation as we age. It is, therefore, essential to keep older adults properly hydrated by monitoring their fluid intake and estimating how much they drink. This paper aims to investigate the effect of surface electromyography (sEMG) features on the detection of drinking events and estimation of the amount of water swallowed per sip. Eleven individuals took part in the study, with data collected over two days. We investigated the best combination of a pool of twenty-six time and frequency domain sEMG features using five classifiers and seven regressors. Results revealed an average F-score over two days of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>77.5</mn><mo>±</mo><mn>1.35</mn><mo>%</mo></mrow></semantics></math></inline-formula> in distinguishing the drinking events from non-drinking events using three global features and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>85.5</mn><mo>±</mo><mn>1.00</mn><mo>%</mo></mrow></semantics></math></inline-formula> using three subject-specific features. The average volume estimation RMSE was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.83</mn><mo>±</mo><mn>0.14</mn></mrow></semantics></math></inline-formula> mL using one single global feature and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.34</mn><mo>±</mo><mn>0.12</mn></mrow></semantics></math></inline-formula> mL using a single subject-specific feature. These promising results validate and encourage the potential use of sEMG as an essential factor for monitoring and estimating the amount of fluid intake. |
| first_indexed | 2024-03-11T11:21:55Z |
| format | Article |
| id | doaj.art-f9bffcb6f0d74bd6a3a13a4dd15fd0ea |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:55Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f9bffcb6f0d74bd6a3a13a4dd15fd0ea2023-11-10T15:12:04ZengMDPI AGSensors1424-82202023-10-012321878910.3390/s23218789A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume EstimationIman Ismail0Imran Khan Niazi1Heidi Haavik2Ernest N. Kamavuako3Department of Engineering, King’s College London, London WC2R 2LS, UKCentre for Chiropractic Research, New Zealand College of Chiropractic, 6 Harisson Road, Mount Wallington, Auckland 1060, New ZealandCentre for Chiropractic Research, New Zealand College of Chiropractic, 6 Harisson Road, Mount Wallington, Auckland 1060, New ZealandDepartment of Engineering, King’s College London, London WC2R 2LS, UKDehydration is a common problem among older adults. It can seriously affect their health and wellbeing and sometimes leads to death, given the diminution of thirst sensation as we age. It is, therefore, essential to keep older adults properly hydrated by monitoring their fluid intake and estimating how much they drink. This paper aims to investigate the effect of surface electromyography (sEMG) features on the detection of drinking events and estimation of the amount of water swallowed per sip. Eleven individuals took part in the study, with data collected over two days. We investigated the best combination of a pool of twenty-six time and frequency domain sEMG features using five classifiers and seven regressors. Results revealed an average F-score over two days of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>77.5</mn><mo>±</mo><mn>1.35</mn><mo>%</mo></mrow></semantics></math></inline-formula> in distinguishing the drinking events from non-drinking events using three global features and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>85.5</mn><mo>±</mo><mn>1.00</mn><mo>%</mo></mrow></semantics></math></inline-formula> using three subject-specific features. The average volume estimation RMSE was <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.83</mn><mo>±</mo><mn>0.14</mn></mrow></semantics></math></inline-formula> mL using one single global feature and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.34</mn><mo>±</mo><mn>0.12</mn></mrow></semantics></math></inline-formula> mL using a single subject-specific feature. These promising results validate and encourage the potential use of sEMG as an essential factor for monitoring and estimating the amount of fluid intake.https://www.mdpi.com/1424-8220/23/21/8789dehydrationelectromyography sensorsEMG featuresfluid intakedrinkingclassification |
| spellingShingle | Iman Ismail Imran Khan Niazi Heidi Haavik Ernest N. Kamavuako A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation Sensors dehydration electromyography sensors EMG features fluid intake drinking classification |
| title | A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation |
| title_full | A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation |
| title_fullStr | A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation |
| title_full_unstemmed | A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation |
| title_short | A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation |
| title_sort | cross day analysis of emg features classifiers and regressors for swallowing events detection and fluid intake volume estimation |
| topic | dehydration electromyography sensors EMG features fluid intake drinking classification |
| url | https://www.mdpi.com/1424-8220/23/21/8789 |
| work_keys_str_mv | AT imanismail acrossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation AT imrankhanniazi acrossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation AT heidihaavik acrossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation AT ernestnkamavuako acrossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation AT imanismail crossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation AT imrankhanniazi crossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation AT heidihaavik crossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation AT ernestnkamavuako crossdayanalysisofemgfeaturesclassifiersandregressorsforswallowingeventsdetectionandfluidintakevolumeestimation |