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AI Employed to Identify Athletes' Emotions with Human-like Accuracy

AI Employed to Identify Athletes' Emotions with Human-like Accuracy

Researchers have managed to harness the power of artificial intelligence to identify affective states in athletes with unprecedented accuracy. The Karlsruhe Institute of Technology (KIT) and the University of Duisburg-Essen have utilized computer-assisted neural networks to develop a model capable of interpreting the body language of tennis players during gameplay. In a landmark event, real game data was used to train the AI model—an approach that resulted in the AI’s ability to assess emotions with human-like precision. However, some ethical concerns have been raised.

The researchers focused their work on a study titled "Recognizing affective states from the expressive behavior of tennis players using convolutional neural networks." With the use of pattern recognition software, they analysed videos of tennis players taken from actual games and produced an exceptional AI model.

This unique AI model has registered a remarkable success rate of 68.9 percent - a rate comparable or even superior to assessments made by human observers and earlier automated methods. The researchers recorded video sequences of 15 tennis players in a specific setting, paying particular attention to the body language displayed when a point was won or lost. The AI system was then trained to associate different body language signals with various emotional responses.

The research findings, while outlining the potential for AI algorithms to surpass human observers in emotion identification, also revealed that both humans and AI have a tendency to recognize negative emotions more readily than positive ones. The researchers stipulated that the distinct expression of negative emotions might make them easier to identify.

Apart from sports applications such as in refining training methods and preventing athlete burnout, this breakthrough could also have significant implications across other fields. These include healthcare, education, customer service, and automotive safety, where early detection of emotional states can provide clear benefits.

Despite these encouraging results, Professor Darko Jekauc from KIT’s Institute of Sports and Sports Science advised caution, highlighting the potential risks around privacy and misuse of data and the need to discuss ethical considerations and establish clear legal guidelines before implementing this technology in practice.

Disclaimer: The above article was written with the assistance of AI. The original sources can be found on ScienceDaily.