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. 2022 Feb 8:12:762396.
doi: 10.3389/fpsyg.2021.762396. eCollection 2021.

Psychological and Emotional Recognition of Preschool Children Using Artificial Neural Network

Zhangxue Rao  1 , Jihui Wu  1 , Fengrui Zhang  2 , Zhouyu Tian  3
Affiliations

Psychological and Emotional Recognition of Preschool Children Using Artificial Neural Network

Zhangxue Rao et al. Front Psychol. .

Abstract

The artificial neural network (ANN) is employed to study children's psychological emotion recognition to fully reflect the psychological status of preschool children and promote the healthy growth of preschool children. Specifically, the ANN model is used to construct the human physiological signal measurement platform and emotion recognition platform to measure the human physiological signals in different psychological and emotional states. Finally, the parameter values are analyzed on the emotion recognition platform to identify the children's psychological and emotional states accurately. The experimental results demonstrate that the recognition ability of children aged 4-6 to recognize the three basic emotions of happiness, calm, and fear increases with age. Besides, there are significant age differences in children's recognition of happiness, calm, and fear. In addition, the effect of 4-year-old children on the theory of mind tasks is less than that of 5- to 6-year-old children, which may be related to more complex cognitive processes. Preschool children are experiencing a stage of rapid emotional development. If children cannot be guided to reasonably identify and deal with emotions at this stage, their education level and social ability development will be significantly affected. Therefore, this study has significant reference value for preschool children's emotional recognition and guidance and can promote children's emotional processing and mental health.

Keywords: artificial neural network; education; emotion recognition; preschool children; traditional education.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Artificial neuron model.
FIGURE 2
FIGURE 2
Structure of the simple feedforward hierarchical ANN.
FIGURE 3
FIGURE 3
Neuron model of the BPNN.
FIGURE 4
FIGURE 4
The typical structure of the BPNN model.
FIGURE 5
FIGURE 5
Double-layer BPNN.
FIGURE 6
FIGURE 6
Overall scheme of the emotion recognition platform.
FIGURE 7
FIGURE 7
Establishment process of the BPNN model.
FIGURE 8
FIGURE 8
Physiological signal characteristic parameters of a subject.
FIGURE 9
FIGURE 9
Recognition results under the calm state.
FIGURE 10
FIGURE 10
Recognition results under the happy state.
FIGURE 11
FIGURE 11
Recognition results under the scared state.
See this image and copyright information in PMC

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