PChCT: A Tool to Monitor Child Whereabouts

Martínez‐Reyes, Fernando; Castro, Luís A.; González-Gurrola, Luis Carlos

doi:10.1007/978-3-319-13105-4_27

Cited by 1 publication

(1 citation statement)

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“…The continuous advancement of technology in recent years, especially in the areas of artificial intelligence and machine learning techniques, has led to the development of systems focused on children’s healthcare in different areas, such as those focused on the diagnosis of diseases through symptom detection [ 5 , 6 ], activity classification systems to detect patterns that indicate disease [ 7 , 8 ], or complete child monitoring systems [ 9 , 10 ], among many more. Most of the works developed for children’s healthcare use multiple sensors as a data source, which are embedded in clothing or carried directly by children in some part of the body.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2021

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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.

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