Evaluation of UX using Biometric Emotion and Intensity Estimation Machine Learning Models

In recent years, user experience (UX) has become an important evaluation criterion in the development of products and services, in addition to the traditional emphasis on functionality and price competitiveness. In addition to practical aspects such as usability and convenience, “emotional value,” which affects the user’s emotions and senses, is also considered an element of UX. Emotional value is the positive feeling that a product or service elicits in the user, resulting in increased satisfaction and purchasing behavior. Thus, emotion is an important component of UX, and based on the “emotional information function theory” of psychology, emotion has been shown to act as the basis for decision-making in situations where judgment cues are scarce. Therefore, accurately understanding emotions and reflecting them in the UX design process is critical for increasing the value of products and services.However, measuring user emotions during UX evaluation remains a significant challenge. Conventional emotion evaluation methods, such as questionnaires and interviews, are primarily subjective; however, they have several limitations. For example, they may not accurately reflect actual emotional states because they are based on respondents’ memories and biases. Furthermore, it is difficult to obtain real-time emotion data because work must be interrupted during the evaluation process. Therefore, using biometric information to evaluate emotions has attracted considerable attention. Biometric information (e.g., heart rate variability, skin electrical response, and EEG ) may be related to users’ emotional responses, reducing subjective bias and allowing real-time evaluation.Furthermore, recent advancements in machine learning technology have stimulated research into emotion estimation using biometric information. In these studies, models have been developed to estimate psychological characteristics such as pleasantness–unpleasantness and arousal–non-arousal of emotions using biometric information, with efforts focusing on Russell’s emotional circle model. Thus, machine learning has shown the ability to connect biometric information with a psychological framework and quantitatively assess the emotional components of UX.This study aimed to build a machine learning model using biometric information, develop a mechanism for estimating user emotions based on Russell’s emotion circle model , and examine whether the model can be applied to UX evaluation. Specifically, we developed a model that uses biological information such as heart rate variability, skin electrical response, and cerebral blood flow as explanatory variables to estimate the emotional position (pleasant–unpleasant, arousal–non-arousal). The accuracy and reliability of the model were evaluated , and the effectiveness of objective UX evaluation using biometric information was determined by comparison with conventional subjective evaluation methods. Through this study , we aim to develop a new evaluation method that can identify emotional factors in UX evaluation in real time and objectively, thereby improving the product and service design process.