Feasibility study of emotion mimicry analysis in human-machine interaction.

Health apps have increased in popularity as people increasingly follow the advice these apps provide to enhance physical and mental well-being. One key aspect of improving neurosensory health is identifying and expressing emotions. Emotional intelligence is crucial for maintaining and enhancing social interactions. In this context, a preliminary closed-loop feedback system has been developed to help people project specific emotions by altering their facial expressions. This system is part of a research intervention aimed at therapeutic applications for individuals with autism spectrum disorder. The proposed system functions as a digital mirror, initially displaying an animated avatar's face expressing a predefined emotion. Users are then asked to mimic the avatar's expression. During this process, a custom emotion recognition model analyzes the user's facial expressions and provides feedback on the accuracy of their projection. A small experimental study involving 8 participants tested the system for feasibility, with avatars projecting the six basic emotions and a neutral expression. The study results indicated a positive correlation between the projected facial expressions and the emotions identified by participants. Participants effectively recognized the emotions, with 85.40% accuracy demonstrating the system's potential in enhancing the well-being of individuals. The participants were also able to mimic the given expression effectively with an accuracy of 46.67%. However, a deficiency in the performance of one of the expressions, surprise, was noticed. In the post processing, this issue was addressed and model enhancements were tailored to boost the performance by ~ 30%. This approach shows promise for therapeutic use and emotional skill development. A further wider experimental study is still required to validate the findings of this study and analyze the impact of modifications made.