A novel approach for food intake detection using electroglottography

Autores
Farooq, Muhammad; Fontana, Juan Manuel; Sazonov, Edward
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Many methods for monitoring diet and food intake rely on subjects self-reporting their daily intake. These methods are subjective, potentially inaccurate and need to be replaced by more accurate and objective methods. This paper presents a novel approach that uses an electroglottograph (EGG) device for an objective and automatic detection of food intake. Thirty subjects participated in a four-visit experiment involving the consumption of meals with self-selected content. Variations in the electrical impedance across the larynx caused by the passage of food during swallowing were captured by the EGG device. To compare performance of the proposed method with a well-established acoustical method, a throat microphone was used for monitoring swallowing sounds. Both signals were segmented into non-overlapping epochs of 30 s and processed to extract wavelet features. Subject-independent classifiers were trained, using artificial neural networks, to identify periods of food intake from the wavelet features. Results from leave-one-out cross validation showed an average per-epoch classification accuracy of 90.1% for the EGG-based method and 83.1% for the acoustic-based method, demonstrating the feasibility of using an EGG for food intake detection.
Fil: Farooq, Muhammad. University of Alabama; Estados Unidos
Fil: Fontana, Juan Manuel. University of Alabama; Estados Unidos. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Mecánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Sazonov, Edward. University of Alabama; Estados Unidos
Materia
EATING DISORDERS
INGESTIVE BEHAVIORS
DIETARY INTAKE MONITORING
ELECTROGLOTTOGRAPHY SENSOR
SUPPORT VECTOR MACHINES
SWALLOWING SOUND
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/102472

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network_name_str CONICET Digital (CONICET)
spelling A novel approach for food intake detection using electroglottographyFarooq, MuhammadFontana, Juan ManuelSazonov, EdwardEATING DISORDERSINGESTIVE BEHAVIORSDIETARY INTAKE MONITORINGELECTROGLOTTOGRAPHY SENSORSUPPORT VECTOR MACHINESSWALLOWING SOUNDhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Many methods for monitoring diet and food intake rely on subjects self-reporting their daily intake. These methods are subjective, potentially inaccurate and need to be replaced by more accurate and objective methods. This paper presents a novel approach that uses an electroglottograph (EGG) device for an objective and automatic detection of food intake. Thirty subjects participated in a four-visit experiment involving the consumption of meals with self-selected content. Variations in the electrical impedance across the larynx caused by the passage of food during swallowing were captured by the EGG device. To compare performance of the proposed method with a well-established acoustical method, a throat microphone was used for monitoring swallowing sounds. Both signals were segmented into non-overlapping epochs of 30 s and processed to extract wavelet features. Subject-independent classifiers were trained, using artificial neural networks, to identify periods of food intake from the wavelet features. Results from leave-one-out cross validation showed an average per-epoch classification accuracy of 90.1% for the EGG-based method and 83.1% for the acoustic-based method, demonstrating the feasibility of using an EGG for food intake detection.Fil: Farooq, Muhammad. University of Alabama; Estados UnidosFil: Fontana, Juan Manuel. University of Alabama; Estados Unidos. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Mecánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Sazonov, Edward. University of Alabama; Estados UnidosIOP Publishing2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/102472Farooq, Muhammad; Fontana, Juan Manuel; Sazonov, Edward; A novel approach for food intake detection using electroglottography; IOP Publishing; Physiological Measurement; 35; 5; 5-2014; 739-7510967-3334CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/0967-3334/35/5/739info:eu-repo/semantics/altIdentifier/doi/10.1088/0967-3334/35/5/739info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:19:59Zoai:ri.conicet.gov.ar:11336/102472instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:19:59.7CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A novel approach for food intake detection using electroglottography
title A novel approach for food intake detection using electroglottography
spellingShingle A novel approach for food intake detection using electroglottography
Farooq, Muhammad
EATING DISORDERS
INGESTIVE BEHAVIORS
DIETARY INTAKE MONITORING
ELECTROGLOTTOGRAPHY SENSOR
SUPPORT VECTOR MACHINES
SWALLOWING SOUND
title_short A novel approach for food intake detection using electroglottography
title_full A novel approach for food intake detection using electroglottography
title_fullStr A novel approach for food intake detection using electroglottography
title_full_unstemmed A novel approach for food intake detection using electroglottography
title_sort A novel approach for food intake detection using electroglottography
dc.creator.none.fl_str_mv Farooq, Muhammad
Fontana, Juan Manuel
Sazonov, Edward
author Farooq, Muhammad
author_facet Farooq, Muhammad
Fontana, Juan Manuel
Sazonov, Edward
author_role author
author2 Fontana, Juan Manuel
Sazonov, Edward
author2_role author
author
dc.subject.none.fl_str_mv EATING DISORDERS
INGESTIVE BEHAVIORS
DIETARY INTAKE MONITORING
ELECTROGLOTTOGRAPHY SENSOR
SUPPORT VECTOR MACHINES
SWALLOWING SOUND
topic EATING DISORDERS
INGESTIVE BEHAVIORS
DIETARY INTAKE MONITORING
ELECTROGLOTTOGRAPHY SENSOR
SUPPORT VECTOR MACHINES
SWALLOWING SOUND
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Many methods for monitoring diet and food intake rely on subjects self-reporting their daily intake. These methods are subjective, potentially inaccurate and need to be replaced by more accurate and objective methods. This paper presents a novel approach that uses an electroglottograph (EGG) device for an objective and automatic detection of food intake. Thirty subjects participated in a four-visit experiment involving the consumption of meals with self-selected content. Variations in the electrical impedance across the larynx caused by the passage of food during swallowing were captured by the EGG device. To compare performance of the proposed method with a well-established acoustical method, a throat microphone was used for monitoring swallowing sounds. Both signals were segmented into non-overlapping epochs of 30 s and processed to extract wavelet features. Subject-independent classifiers were trained, using artificial neural networks, to identify periods of food intake from the wavelet features. Results from leave-one-out cross validation showed an average per-epoch classification accuracy of 90.1% for the EGG-based method and 83.1% for the acoustic-based method, demonstrating the feasibility of using an EGG for food intake detection.
Fil: Farooq, Muhammad. University of Alabama; Estados Unidos
Fil: Fontana, Juan Manuel. University of Alabama; Estados Unidos. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Mecánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Sazonov, Edward. University of Alabama; Estados Unidos
description Many methods for monitoring diet and food intake rely on subjects self-reporting their daily intake. These methods are subjective, potentially inaccurate and need to be replaced by more accurate and objective methods. This paper presents a novel approach that uses an electroglottograph (EGG) device for an objective and automatic detection of food intake. Thirty subjects participated in a four-visit experiment involving the consumption of meals with self-selected content. Variations in the electrical impedance across the larynx caused by the passage of food during swallowing were captured by the EGG device. To compare performance of the proposed method with a well-established acoustical method, a throat microphone was used for monitoring swallowing sounds. Both signals were segmented into non-overlapping epochs of 30 s and processed to extract wavelet features. Subject-independent classifiers were trained, using artificial neural networks, to identify periods of food intake from the wavelet features. Results from leave-one-out cross validation showed an average per-epoch classification accuracy of 90.1% for the EGG-based method and 83.1% for the acoustic-based method, demonstrating the feasibility of using an EGG for food intake detection.
publishDate 2014
dc.date.none.fl_str_mv 2014-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/102472
Farooq, Muhammad; Fontana, Juan Manuel; Sazonov, Edward; A novel approach for food intake detection using electroglottography; IOP Publishing; Physiological Measurement; 35; 5; 5-2014; 739-751
0967-3334
CONICET Digital
CONICET
url http://hdl.handle.net/11336/102472
identifier_str_mv Farooq, Muhammad; Fontana, Juan Manuel; Sazonov, Edward; A novel approach for food intake detection using electroglottography; IOP Publishing; Physiological Measurement; 35; 5; 5-2014; 739-751
0967-3334
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/0967-3334/35/5/739
info:eu-repo/semantics/altIdentifier/doi/10.1088/0967-3334/35/5/739
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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