Children activity recognition (CAR) is a subject for which numerous works have been developed in recent years, most of them focused on monitoring and safety. Commonly, these works use as data source different types of sensors that can interfere with the natural behavior of children, since these sensors are embedded in their clothes. This article proposes the use of environmental sound data for the creation of a children activity classification model, through the development of a deep artificial neural network (ANN). Initially, the ANN architecture is proposed, specifying its parameters and defining the necessary values for the creation of the classification model. The ANN is trained and tested in two ways: using a 70–30 approach (70% of the data for training and 30% for testing) and with a k-fold cross-validation approach. According to the results obtained in the two validation processes (70–30 splitting and k-fold cross validation), the ANN with the proposed architecture achieves an accuracy of 94.51% and 94.19%, respectively, which allows to conclude that the developed model using the ANN and its proposed architecture achieves significant accuracy in the children activity classification by analyzing environmental sound.

中文翻译：

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基于环境声音数据的深度人工神经网络生成儿童活动分类模型

儿童活动识别（CAR）是近年来开发的众多研究成果的一个主题，其中大多数集中在监测和安全方面。通常，这些作品使用不同类型的传感器作为数据源，这些传感器可能会干扰儿童的自然行为，因为这些传感器嵌入在他们的衣服中。本文建议通过开发深度人工神经网络 (ANN)，使用环境声音数据创建儿童活动分类模型。最初，提出了 ANN 架构，指定其参数并定义创建分类模型所需的值。ANN 通过两种方式进行训练和测试：使用 70-30 方法（70% 的数据用于训练，30% 用于测试）和 k 折交叉验证方法。根据两个验证过程（70-30 分割和 k 倍交叉验证）中获得的结果，采用所提出的架构的 ANN 分别达到了 94.51% 和 94.19% 的准确率，这可以得出结论，使用所开发的模型人工神经网络及其提出的架构通过分析环境声音，在儿童活动分类方面实现了显着的准确性。