Cognitive physiological data analysis based on the XGBoost algorithm to realize positive perceptual sample classification

Abstract

Designers refer to existing product cases and innovate products to develop new products. However, when designers screen product cases, there is no user participation, which leads to the lack of user-side knowledge and emotional drive that is very important for design. Therefore, it is necessary to play the role of user emotional knowledge in promoting the whole design process. This paper proposes the concept of the positive perceptual sample, which applies the knowledge emotion integration of designers and users to the screening sample case stage at the beginning of the design process. This study is based on the lack of user-side knowledge and emotional drive of reference cases and integrates user emotion into the reference case screening process. Then, in the emotion measurement process, users’ cognitive data in the screening process are obtained through the eye-brain fusion cognitive experiment. Finally, the XGBoost algorithm is used to process feature index data to realize the classification and recognition of cognitive data and applied to the positive perceptual classification of products. The results show that the classification accuracy of physiological cognitive data with user emotional representation by the XGBoost algorithm is 90.87% . The results of cognitive data classification are applied to the screening of positive perceptual samples, and the satisfaction rate is 98.35% . The results show that the method proposed in this paper provides a new source of ideas for obtaining positive perceptual samples and can be applied to new product development.

E-MFNN: an emotion-multimodal fusion neural network framework for emotion recognition

“…Emotional recognition simulates and recognizes human emotions through computer technology ( Ezzameli & Mahersia, 2023 ). Emotional computing combines disciplines such as computer science, psychology, neuroscience, and linguistics to analyze and process various information such as human speech, facial expressions, and physiological indicators through computers, thereby inferring human emotional states and behaviors ( Guo & Lin, 2023 ). In recent years, emotion recognition has received increasing attention due to its applications in various research fields such as human-computer interaction, emotion analysis, user behavior detection, and fatigue monitoring ( Zhao et al, 2022 ).…”