Combining Design Neurocognition Technologies and Neural Networks to Evaluate and Predict New Product Designs: A Multimodal Human–Computer Interaction Study

Abstract

The multimodal data collection that includes physiological and psychological data, combined with data processing using artificial intelligence technology, has become a research trend in human–computer interaction. In the stage of new product design, it is necessary to consider user experience for the evaluation and prediction of new products. The paper presents a human–computer interaction study on new product design with user participation. This research adopts a combination of design neurocognition and genetic algorithms in design optimization to evaluate the usability of engineering control interfaces using eye-tracking and facial expression data. Eye-tracking and neural network technology are used to predict the appearance of humanoid robots. The paper explored the evaluation and prediction of new product design using multimodal physiological and psychological data. The research results indicate that artificial intelligence technologies represented by neural networks can fully exploit biometric data represented by eye-tracking and facial expression, improving the effectiveness of new product evaluation and prediction accuracy. The research results provide a solution based on the combination of design neurocognition and artificial intelligence technology for the evaluation and prediction of new product design in the future.