Recognizing affective states from the expressive behavior of tennis players using convolutional neural networks

This study describes an AI model by leveraging advanced Convolutional Neural Networks (CNNs) to recognize affective states in real-world sports settings, particularly tennis matches. In contrast to prior studies that primarily utilized data acquired from actors and rudimentary statistical methods, the present research emphasizes the analysis of bodily expressions in real-life contexts, aiming for a more naturalistic representation of human emotions. Our CNN-based models demonstrate an accuracy rate of up to 68.9 %, outperforming or matching human observers in many instances. Intriguingly, both the machine learning models and human observers exhibited a shared propensity to more effectively identify negative affective states, which may be attributed to the more intense and straightforward expression of these states. These results not only advance the state of the art in affective state recognition but also pave the way for broader applications, including in healthcare and automotive safety sectors, thereby constituting a significant advancement in the development of sophisticated and universally applicable emotional recognition systems.