The recent global increase in average life expectancy has led to an inevitable increase in the elderly population (22% in 2050), with an (unrelated) reduction in the young population. The increase in life expectancy leads to negative consequences due to the onset of diseases related to aging causing physical and social disabilities of the elderly. This condition leads to the need for hospitalizations (with a consequent increase in the costs at the expense of the sanitary system) or 24/7 care in dedicated facilities that often suffer from dedicated specialized personnel shortage. Home care through dedicated caregivers is also following the same trend. The context of Ambient Assisted Living (AAL) proposes a solution to the problem by equipping domestic or assistive infrastructures with intelligent sensor networks scattered throughout the environment, and robotic platforms (i.e., AAL robots). Moreover, the employment of the latter recorded a drastic increase (>20%) in the last two years due to the pandemic emergency. In this context, this Ph.D. thesis presents a novel robot-empowered AAL infrastructure capable of creating a pervasive environment, which is fully-aware of the psychophysical status of the patient and that adapts to different degrees of disability and emotional involvement of the patient. Specifically, the features introduced by this thesis in the context of an AAL infrastructure are: (i) the introduction of a Brain-Computer Interface (BCI) for the selection of services, able to allow patients with severe disabilities to formalize requests; (ii) the incorporation of a cognitive status recognition system based on electroencephalographic (EEG) signals able to provide monitoring parameters and support for remote diagnosis; (iii) the inclusion in the infrastructure of an emotion recognition system capable of triggering an adaptive behavioral system by the robot component of the architecture. Concerning the BCI-based selection system, two neural interfaces are proposed. A BCI exploits movement-related ...
Design and Implementation of a Novel EEG-based Brain-Computer Interface to Improve "Perception - Understanding - Action" in Humanoid Robotics
2022-01-01
doi:10.60576/poliba/iris/mezzina-giovanni_phd2022
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |