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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/102472
Ver los metadatos del registro completo
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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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1844614176402571264 |
score |
13.070432 |