Researchers at Karlsruhe Institute of Technology and the University of Duisburg-Essen have trained an AI model to accurately identify affective states from the body language of tennis players during games.

Their study, published in the journal Knowledge-Based Systems, demonstrates that AI can assess body language and emotions with an accuracy similar to that of humans.

Accuracy comparable to that of human observers

“Our model can identify affective states with an accuracy of up to 68.9 percent, which is comparable and sometimes even superior to assessments made by both human observers and earlier automated methods,” said Professor Darko Jekauc of Karlsruhe Institute of Technology Institute of Sports and Sports Science in a statement.

“The reason [for this accuracy] could be that negative emotions are easier to identify because they’re expressed in more obvious ways,” said Jekauc. “Psychological theories suggest that people are evolutionarily better adapted to perceive negative emotional expressions, for example, because defusing conflict situations quickly is essential to social cohesion.”

Body language clues

The researchers recorded video sequences of 15 tennis players in a specific setting, focusing on the body language displayed when a point was won or lost. The videos showed players with cues including lowered head, arms raised in exultation, hanging racket, or differences in walking speed; these cues could be used to identify the players’ affective states. 

After being fed this data, the AI learned to associate the body language signals with different affective reactions and to determine whether a point had been won (positive body language) or lost (negative body language). “Training in natural contexts is a significant advance for the identification of real emotional states, and it makes predictions possible in real scenarios,” said Jekauc.

Uses of reliable emotion recognition

The researchers envision a number of sports applications for reliable emotion recognition, such as improving training methods, team dynamics and performance, and preventing burnout. Other fields, including healthcare, education, customer service and automotive safety, could also benefit from reliable early detection of emotional states.

Citation: Darko Jekauc, Diana Burkart, Julian Fritsch, Marc Hesenius, Ole Meyer, Saquib Sarfraz, Rainer Stiefelhagen. Recognizing affective states from the expressive behavior of tennis players using convolutional neural networks. Knowledge-Based Systems, Vol. 295, 2024. DOI: 10.1016/j.knosys.2024.111856 (open access)

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