Homepage Inria website

Section: New Results

VR and Sports

Participants : Richard Kulpa, Benoit Bideau [contact] , Brault Sébastien, Burns Anne-Marie.

In the past, we have worked on the interaction between two opponents in handball. We have designed a framework to animate virtual throwers in a reality center and to analyze the gestures of real goalkeepers whose objective was to intercept the corresponding virtual balls. This VR framework was then validated by showing that behaviors in real and virtual environment were similar. These works have been extended by using perception-action coupling and perception-only studies to evaluate the anticipation of opponents. In order to evaluate the importance of perceived parameters, the ball and/or the character animation was successively hidden to determine their importance and the same kind of study was done on the graphical level of details.

These works have been extended to the study of deceptive movements and gaps evaluation in rugby. Combining perceptual analysis based on the use of cutoffs with biomechanical analysis, we have extracted important kinematic information that could explain differences between experts and novices. Indeed, thanks to the cutoffs, it is possible to determine how early each of these two levels of practice can perceive the correct final direction of the opponent. Then this information is correlated to kinematical parameters of this player. Finally, we have embedding these knowledge on the evaluation of novices and expertsd to create models of rugby defenders. We are currently working on integrating these models in a VR experiment in which the real user is this time the attacker and our model the virtual defender.

Concurrently, studies are experimented to determine if VR can be used for training in sports [9] . The first step was to compare if trainees learned the same way in real situation, facing a video of the lesson or facing a virtual teacher that is animated from the motions of the real course. Based on evaluation of an expert, the results showed that the three groups evolved the same way and reached the same level of practice. The second step is then to have more experts to complete the evaluation but also to combine these results with objective analyses based on kinematics data.

This work is partially funded by the Biofeedback project (DGCIS "Serious Gaming" project) of the M2S laboratory, University Rennes 2. Its goal was to create a training tool that can be used and configured by coaches in order to train athletes to repetitive motions such as katas in karate. The evaluation is made by an automatic module that compares the kinematics of the trainee to a database of expert movements.