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.
a model descriptive of reaching movement in static and dynamic conditions
a research paper submitted on a relevant journal of the sector
- Università di Bologna (UNIBO), Patrizia Fattori
- German Research Centre for Artificial Intelligence (DFKI), Elsa Kirchner
Primary Contact: Patrizia Fattori, University of Bologna