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Article

Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

by
Antonio García-Domínguez
1,
Carlos E. Galván-Tejada
1,*,
Ramón F. Brena
2,
Antonio A. Aguileta
3,
Jorge I. Galván-Tejada
1,
Hamurabi Gamboa-Rosales
1,
José M. Celaya-Padilla
1 and
Huizilopoztli Luna-García
1
1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro 98000, Zacatecas, Mexico
2
Tecnológico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Nuevo León, Mexico
3
Facultad de Matemáticas, Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tablaje Cat. 13615, Colonia Chuburná Hidalgo Inn, Mérida 97110, Yucatan, Mexico
*
Author to whom correspondence should be addressed.
Healthcare 2021, 9(7), 884; https://doi.org/10.3390/healthcare9070884
Submission received: 30 April 2021 / Revised: 26 June 2021 / Accepted: 6 July 2021 / Published: 13 July 2021
(This article belongs to the Special Issue Nursing, Child and Pediatric Health)
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Abstract

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.
Keywords: children’s activity classification; environmental sound; domestic accidents; Bayesian network children’s activity classification; environmental sound; domestic accidents; Bayesian network

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MDPI and ACS Style

García-Domínguez, A.; Galván-Tejada, C.E.; Brena, R.F.; Aguileta, A.A.; Galván-Tejada, J.I.; Gamboa-Rosales, H.; Celaya-Padilla, J.M.; Luna-García, H. Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network. Healthcare 2021, 9, 884. https://doi.org/10.3390/healthcare9070884

AMA Style

García-Domínguez A, Galván-Tejada CE, Brena RF, Aguileta AA, Galván-Tejada JI, Gamboa-Rosales H, Celaya-Padilla JM, Luna-García H. Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network. Healthcare. 2021; 9(7):884. https://doi.org/10.3390/healthcare9070884

Chicago/Turabian Style

García-Domínguez, Antonio, Carlos E. Galván-Tejada, Ramón F. Brena, Antonio A. Aguileta, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, José M. Celaya-Padilla, and Huizilopoztli Luna-García. 2021. "Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network" Healthcare 9, no. 7: 884. https://doi.org/10.3390/healthcare9070884

APA Style

García-Domínguez, A., Galván-Tejada, C. E., Brena, R. F., Aguileta, A. A., Galván-Tejada, J. I., Gamboa-Rosales, H., Celaya-Padilla, J. M., & Luna-García, H. (2021). Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network. Healthcare, 9(7), 884. https://doi.org/10.3390/healthcare9070884

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