Author(s): Jack Thomson*
The prediction of continuous emotional measures through physiological and visual data is an emerging field that aims to understand and predict human emotions more accurately and reliably. Traditional self-reporting methods for assessing emotions have limitations in capturing the dynamic nature of emotions. This article explores the potential of physiological signals, such as heart rate and electrodermal activity, and visual data, including facial expressions and body language, for predicting emotional states. Machine learning techniques, such as supervised learning and feature fusion, are utilized to develop models that analyze and interpret these data sources. The integration of physiological and visual data offers a more comprehensive understanding of emotional states and has applications in healthcare, human-computer interaction, marketing, and more. While challenges remain, such as data collection and model interpretability, the prediction of continuous emotional measures holds great promise for improving mental health, personalized experiences, and overall well-being.
The Journal of International Social Research received 8982 citations as per Google Scholar report
