<abstract xmlns="http://www.w3.org/1999/xhtml">

Although many kinds of intelligent toys, interactive toys that can mobilize children’s sensory experience in many aspects and cultivate their creative thinking are still scarce. In this paper, interactive experience elements are built based on virtual reality technology, image recognition technology, human-computer interaction theory, and ergonomics theory. A brain interaction network based on multimodal sensory modulation is constructed to allow intelligent toys to perceive their surroundings effectively. And the dynamic causal model (DCM) is proposed to infer the connection strength and direction of neural activities among pre-defined cortical regions of the brain. The results showed that the classification accuracy of noisy audiovisual (AVd) modality in the BA18 region was 87.87% significantly higher than that of the STS region (81.41%). The average classification accuracy of the mono-visual noise-added (Vd) modality in the BA18 ROI was 79.40%, which was also significantly higher than that of the STS ROI at 65.12%. It indicates that the interactive design based on multimodal senses can effectively stimulate children’s brain interest areas, which makes children experience better and communicate more interactions. The research results of this paper provide new perspectives and have important implications for the application of interactive intelligence to toy art.
</abstract>

Although many kinds of intelligent toys, interactive toys that can mobilize children’s sensory experience in many aspects and cultivate their creative thinking are still scarce. In this paper, interactive experience elements are built based on virtual reality technology, image recognition technology, human-computer interaction theory, and ergonomics theory. A brain interaction network based on multimodal sensory modulation is constructed to allow intelligent toys to perceive their surroundings effectively. And the dynamic causal model (DCM) is proposed to infer the connection strength and direction of neural activities among pre-defined cortical regions of the brain. The results showed that the classification accuracy of noisy audiovisual (AVd) modality in the BA18 region was 87.87% significantly higher than that of the STS region (81.41%). The average classification accuracy of the mono-visual noise-added (Vd) modality in the BA18 ROI was 79.40%, which was also significantly higher than that of the STS ROI at 65.12%. It indicates that the interactive design based on multimodal senses can effectively stimulate children’s brain interest areas, which makes children experience better and communicate more interactions. The research results of this paper provide new perspectives and have important implications for the application of interactive intelligence to toy art.

Research on the design of interactive intelligent toy art experience based on multimodal sensory

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Although many kinds of intelligent toys, interactive toys that can mobilize children’s sensory experience in many aspects and cultivate their creative thinking...

Research on the design of interactive intelligent toy art experience based on multimodal sensory

Published Online: Sep 20, 2023

Page range: -

Received: Dec 19, 2022

Accepted: Mar 31, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00351

Keywords
Cortical regions, Interactive experience, Multimodal sensory, Brain interaction network, Dynamic causal model

© 2023 Cong Luo, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the design of interactive intelligent toy art experience based on multimodal sensory

Published Online: Sep 20, 2023

Page range: -

Received: Dec 19, 2022

Accepted: Mar 31, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00351

KeywordsCortical regions, Interactive experience, Multimodal sensory, Brain interaction network, Dynamic causal model

© 2023 Cong Luo, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Cortical regions, Interactive experience, Multimodal sensory, Brain interaction network, Dynamic causal model