Neurorobotics and Strategies for Adaptive Human-machine Interaction

According to the world health organization (WHO), about 15% of the world population suffer from a disability that limits their ability to execute various Activities of Daily Living (ADLs). Assistive and rehabilitative neurorobotics provides great potential for these individuals to regain their independence and return to being an active part of society. Since technology is now evolving to a level where robots can interact with humans in their daily life, such systems must be able to equally interact with people of varying abilities. Therefore, reliable and intuitive human-machine interfaces customized to the user’s particular needs and based on his/her residual capabilities are very important for maximizing system usability and user acceptance. To have a customizable solution based on the specific needs of the user, the design and interaction management between complex systems are the most challenging aspects in the development of assistive neurorobotics. At the same time, human-machine interaction has to account for the adaptive capacity and plasticity of neural systems. This research topic will provide an overview of the state-of-the-art in assistive neurorobotic technologies and present the latest results on systems and strategies for monitoring and reconstructing patient state and intention during human-machine interaction.

Covered topics will be:
· User intention detection from neural signals
· Brain signal decoding
· User state estimation
· Brain-computer interface for human-robot-interaction
· Brain-machine interfaces (BMI) for motor rehabilitation
· Invasive and non-invasive interfaces
· Assistive neurorobotics, prostheses and biomechanics
· Robot-aided rehabilitation to trigger neuroplasticity and neurorestoration
· Robot control through interfaces
· Sensory feedback
· Adaptive brain stimulation