Scenario Description
SHARESPACE provides a unique experience in Shared Hybrid Space (SHS), mixing real and AI-driven users. This is an original and efficient mean to train people in complex coordination and synchronization tasks. In the specific case of sports training, this possibility to make real and virtual users collaborate opens new possibilities: manipulating multisensory feedback sent to the users, controlling the AI-driven users to facilitate the coordination tasks by amplifying visual cues. In group sports training, such as cycling in a peloton, one of the skills used to enhance the group performance, is anticipation: being able to anticipate sudden events of participants’ actions that could alter the coordination with the others. In real training, cyclers have to mobilize several people to recreate/mimic these complex coordination patterns, and this is very difficult to accurately control this situation.
Inria, with the help of the M2S Lab from University Rennes2 has a long experience in developing VR training systems for sports, and regularly hosts elite athletes to develop dedicated systems with real transfer of skills between VR training and real practice. In collaboration with experts in sports cycling, we have identified a scenario in which SHS could help cyclers to improve their performance: accompany a breakaway in a peloton. In this situation, a cyclist is placed in a peloton with competitors. At certain moments, the virtual opponent positioned in front of him will suddenly try to accelerate to break away from the peloton. The trained cyclist must be able to rapidly detect this attack to react quickly and stay at a very close distance of the opponent to benefit from low friction air.
SHS offer users the opportunity to train for this situation against AI-driven competitor. More important, the attacker’s movements can be amplified to help users learn to detect early signs of an attack. This amplification can be more or less important along the training sessions, until it is not necessary any more, to correctly and early detect the attack for the participants. Practically, the participant could train from home, using his own bike mounted on a home-trainer with only one sensor placed on the bike crankset and a Head Mounted Display. He can realistically cycle on his home trainer while looking at the AI-driven character, to train his anticipation skills in this specific scenario.
Read all about the results of the Sport Scenario here.
