Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach

Autores
Saavedra, Marcos David; Inthamoussou, Fernando Ariel; Fushimi, Emilia; Garelli, Fabricio
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Individuals with Type 1 Diabetes (T1D) require close glucose monitoringto prevent both short- and long-term complications. Physical activity (PA) is a significantsource of variability in metabolic dynamics, leading to glycemic fluctuations that depend onthe type, intensity, and duration of the exercise. Accurately monitoring and classifying thetype of PA is crucial for optimizing glycemic control and minimizing the risk of hypoglycemia.Methods: This study utilizes the largest clinical trial of PA in people with T1D to date,the Type 1 Diabetes and Exercise Initiative (T1DEXI), which included both structured andunstructured PA sessions, to develop an online classification approach for identifying thetype of PA (aerobic, interval, resistance). A computationally efficient Convolutional NeuralNetwork (CNN) was trained on time-frequency representations (spectrograms) of step countand heart rate signals, readily available from wearable devices, from the structured PAsessions of the T1DEXI dataset. The proposed methodology presents an ad-hoc processfor designing the spectrograms based on the CNN architecture to optimize the classifier’sperformance.Results: The CNN-based classification approach was implemented using spectrogramsof 5 and 30-minute signals, resulting in two classifiers that achieve high classification accuracywhen evaluated on the structured PA sessions. The 5-minute classifier was then applied tounstructured PA sessions, where the predicted distribution of glucose changes for the activitytypes was consistent with clinical evidence.Conclusion: These results demonstrate the potential of the proposed approach for itsintegration into decision support systems or automated insulin delivery systems, enablingimproved glucose management during exercise in T1D.
Fil: Saavedra, Marcos David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Inthamoussou, Fernando Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Fushimi, Emilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Materia
Physical Activity
Classification
Convolutional Neural Network
Spectrogram
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/276169
Acceder
id CONICETDig_96b972f53bfb8e6e1d31fb79246f9e12
oai_identifier_str oai:ri.conicet.gov.ar:11336/276169
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based ApproachSaavedra, Marcos DavidInthamoussou, Fernando ArielFushimi, EmiliaGarelli, FabricioPhysical ActivityClassificationConvolutional Neural NetworkSpectrogramhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Background: Individuals with Type 1 Diabetes (T1D) require close glucose monitoringto prevent both short- and long-term complications. Physical activity (PA) is a significantsource of variability in metabolic dynamics, leading to glycemic fluctuations that depend onthe type, intensity, and duration of the exercise. Accurately monitoring and classifying thetype of PA is crucial for optimizing glycemic control and minimizing the risk of hypoglycemia.Methods: This study utilizes the largest clinical trial of PA in people with T1D to date,the Type 1 Diabetes and Exercise Initiative (T1DEXI), which included both structured andunstructured PA sessions, to develop an online classification approach for identifying thetype of PA (aerobic, interval, resistance). A computationally efficient Convolutional NeuralNetwork (CNN) was trained on time-frequency representations (spectrograms) of step countand heart rate signals, readily available from wearable devices, from the structured PAsessions of the T1DEXI dataset. The proposed methodology presents an ad-hoc processfor designing the spectrograms based on the CNN architecture to optimize the classifier’sperformance.Results: The CNN-based classification approach was implemented using spectrogramsof 5 and 30-minute signals, resulting in two classifiers that achieve high classification accuracywhen evaluated on the structured PA sessions. The 5-minute classifier was then applied tounstructured PA sessions, where the predicted distribution of glucose changes for the activitytypes was consistent with clinical evidence.Conclusion: These results demonstrate the potential of the proposed approach for itsintegration into decision support systems or automated insulin delivery systems, enablingimproved glucose management during exercise in T1D.Fil: Saavedra, Marcos David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Inthamoussou, Fernando Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Fushimi, Emilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaLiebert, Mary Ann2025-07info: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/11336/276169Saavedra, Marcos David; Inthamoussou, Fernando Ariel; Fushimi, Emilia; Garelli, Fabricio; Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach; Liebert, Mary Ann; Diabetes Technology and Obesity Medicine; 1; 1; 7-2025; 361-3732998-6702CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1177/29941520251358842info:eu-repo/semantics/altIdentifier/url/https://www.liebertpub.com/doi/10.1177/29941520251358842info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-12-03T09:01:15Zoai:ri.conicet.gov.ar:11336/276169instacron: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:34982025-12-03 09:01:16.074CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
title Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
spellingShingle Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
Saavedra, Marcos David
Physical Activity
Classification
Convolutional Neural Network
Spectrogram
title_short Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
title_full Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
title_fullStr Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
title_full_unstemmed Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
title_sort Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach
dc.creator.none.fl_str_mv Saavedra, Marcos David
Inthamoussou, Fernando Ariel
Fushimi, Emilia
Garelli, Fabricio
author Saavedra, Marcos David
author_facet Saavedra, Marcos David
Inthamoussou, Fernando Ariel
Fushimi, Emilia
Garelli, Fabricio
author_role author
author2 Inthamoussou, Fernando Ariel
Fushimi, Emilia
Garelli, Fabricio
author2_role author
author
author
dc.subject.none.fl_str_mv Physical Activity
Classification
Convolutional Neural Network
Spectrogram
topic Physical Activity
Classification
Convolutional Neural Network
Spectrogram
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Background: Individuals with Type 1 Diabetes (T1D) require close glucose monitoringto prevent both short- and long-term complications. Physical activity (PA) is a significantsource of variability in metabolic dynamics, leading to glycemic fluctuations that depend onthe type, intensity, and duration of the exercise. Accurately monitoring and classifying thetype of PA is crucial for optimizing glycemic control and minimizing the risk of hypoglycemia.Methods: This study utilizes the largest clinical trial of PA in people with T1D to date,the Type 1 Diabetes and Exercise Initiative (T1DEXI), which included both structured andunstructured PA sessions, to develop an online classification approach for identifying thetype of PA (aerobic, interval, resistance). A computationally efficient Convolutional NeuralNetwork (CNN) was trained on time-frequency representations (spectrograms) of step countand heart rate signals, readily available from wearable devices, from the structured PAsessions of the T1DEXI dataset. The proposed methodology presents an ad-hoc processfor designing the spectrograms based on the CNN architecture to optimize the classifier’sperformance.Results: The CNN-based classification approach was implemented using spectrogramsof 5 and 30-minute signals, resulting in two classifiers that achieve high classification accuracywhen evaluated on the structured PA sessions. The 5-minute classifier was then applied tounstructured PA sessions, where the predicted distribution of glucose changes for the activitytypes was consistent with clinical evidence.Conclusion: These results demonstrate the potential of the proposed approach for itsintegration into decision support systems or automated insulin delivery systems, enablingimproved glucose management during exercise in T1D.
Fil: Saavedra, Marcos David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Inthamoussou, Fernando Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Fushimi, Emilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
Fil: Garelli, Fabricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentina
description Background: Individuals with Type 1 Diabetes (T1D) require close glucose monitoringto prevent both short- and long-term complications. Physical activity (PA) is a significantsource of variability in metabolic dynamics, leading to glycemic fluctuations that depend onthe type, intensity, and duration of the exercise. Accurately monitoring and classifying thetype of PA is crucial for optimizing glycemic control and minimizing the risk of hypoglycemia.Methods: This study utilizes the largest clinical trial of PA in people with T1D to date,the Type 1 Diabetes and Exercise Initiative (T1DEXI), which included both structured andunstructured PA sessions, to develop an online classification approach for identifying thetype of PA (aerobic, interval, resistance). A computationally efficient Convolutional NeuralNetwork (CNN) was trained on time-frequency representations (spectrograms) of step countand heart rate signals, readily available from wearable devices, from the structured PAsessions of the T1DEXI dataset. The proposed methodology presents an ad-hoc processfor designing the spectrograms based on the CNN architecture to optimize the classifier’sperformance.Results: The CNN-based classification approach was implemented using spectrogramsof 5 and 30-minute signals, resulting in two classifiers that achieve high classification accuracywhen evaluated on the structured PA sessions. The 5-minute classifier was then applied tounstructured PA sessions, where the predicted distribution of glucose changes for the activitytypes was consistent with clinical evidence.Conclusion: These results demonstrate the potential of the proposed approach for itsintegration into decision support systems or automated insulin delivery systems, enablingimproved glucose management during exercise in T1D.
publishDate 2025
dc.date.none.fl_str_mv 2025-07
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/11336/276169
Saavedra, Marcos David; Inthamoussou, Fernando Ariel; Fushimi, Emilia; Garelli, Fabricio; Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach; Liebert, Mary Ann; Diabetes Technology and Obesity Medicine; 1; 1; 7-2025; 361-373
2998-6702
CONICET Digital
CONICET
url http://hdl.handle.net/11336/276169
identifier_str_mv Saavedra, Marcos David; Inthamoussou, Fernando Ariel; Fushimi, Emilia; Garelli, Fabricio; Identification of Physical Activity Type in People with Diabetes: A Spectrogram-based Approach; Liebert, Mary Ann; Diabetes Technology and Obesity Medicine; 1; 1; 7-2025; 361-373
2998-6702
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1177/29941520251358842
info:eu-repo/semantics/altIdentifier/url/https://www.liebertpub.com/doi/10.1177/29941520251358842
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Liebert, Mary Ann
publisher.none.fl_str_mv Liebert, Mary Ann
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
_version_ 1850505192410185728
score 13.214268

Publicaciones similares