COMBAHO: A deep learning system for integrating brain injury patients in society
José García‐Rodríguez, Francisco Gomez‐Donoso, Sergiu Oprea, Alberto García-García, Miguel Cazorla, Sergio Orts‐Escolano, Zuria Bauer, John Alejandro Castro-Vargas, Félix Escalona, David Ivorra-Piqueres, Pablo Martínez-González, Eugenio Aguirre, Miguel García-Silvente, Marcelo García-Pérez, Jose M. Cañas, Francisco Martín, Jonatan Ginés, F.M. Rivas-Montero · Pattern Recognition Letters · 2019
In the last years, the care of dependent people, either by disease, accident, disability, or age, is one of the current priority research topics in developed countries. Moreover, such care is intended to be at patients home, in order to minimize the cost of therapies. Patients rehabilitation will be fulfilled when their integration in society is achieved, either in the family or in a work environment. To address this challenge, we propose the development and evaluation of an assistant for people with acquired brain injury or dependents. This assistant is twofold: in the patient’s home is based on the design and use of an intelligent environment with abilities to monitor and active learning, combined with an autonomous social robot for interactive assistance and stimulation. On the other hand, it is complemented with an outdoor assistant, to help patients under disorientation or complex situations. This involves the integration of several existing technologies and provides solutions to a variety of technological challenges. Deep leaning-based techniques are proposed as core technology to solve these problems.