HumaneAINet

Human AI Vision of European Artificial Intelligence

Microproject: Prediction of static and perturbed reach goals from movement kinematics

Reaching movements towards targets located in the 3-dimensional space are fast and accurate. Although they may seem simple and natural movements, they imply the integration of different sensory information that is carried in real time by our brain. We will apply machine learning techniques to address different questions as it follows: i) at which point of the movement is it accurately possible to predict the final target goal in static and dynamic conditions? ii) as at behavioural level it was hypothesized that direction and depth dimension do not rely on shared networks in the brain during the execution of movement but they are processed separately, can the targets located along the horizontal or sagittal dimension be predicted with the same or different accuracy? Finally, we will frame our result in the context of improving user-agent interactions, moving from a description of human movement to a possible implementation in social/collaborative AI.