A Framework for Optimizing AI-Based Virtual Reality: A Use Case in Sport Sciences

Research in sport sciences needs realistic simulators to elicit natural behaviors and understand the information-movement coupling underlying performance. This article outlines the theoretical and methodological considerations for optimizing the use of artificial intelligence (AI) in the development of a virtual reality setup (VR) for sport science use. The use case focused on the study information-movement coupling in a dyadic basket-throwing situation. An optimized software pipeline combining Google's MediaPipe SSD networks to capture the movement of a real attacker attempting to score in a virtual basket, and LSTM networks to classify the attacker's intentions and “intelligently” select from a library the animation of the defender's avatar was developed. As a result, the defender's avatar behaved on the basis of the behavior of a real attacker to contest the throw only if the attacker performed a guenuine throw. Measurements of two contradictory criteria (recognition rate of the attacker's intention and computation time) allowed to validate the optimized pipeline. The perspectives in the improvement of the simulator's realism, but also for theoretical, methodological, and applied viewpoints are discussed.