COMBAHO: A deep learning system for integrating brain injury patients in society
Metadatos
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García Rodríguez, Jose; Gómez Donoso, Francisco; Oprea, Sergio; García García, Alberto; Cazorla, Miguel; Orts Escolano, Sergio; Bauer, Zuria; Castro Vargas, John; Escalona, Felix; Ivorra Piqueres, David; Martínez González, Pablo; Aguirre Molina, Eugenio; García Silvente, Miguel; García Pérez, Marcelo; Cañas, Jose María; Martín Rico, Francisco; Gines, Jonathan; Rivas Montero, FranciscoEditorial
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Materia
Robot assistants Ambient assisted living Rehabilitation aids
Fecha
2020-09Referencia bibliográfica
Jose Garcia-Rodriguez, Francisco Gomez-Donoso, Sergiu Oprea, Alberto Garcia-Garcia, Miguel Cazorla, Sergio Orts-Escolano, Zuria Bauer, John Castro-Vargas, Felix Escalona, David Ivorra-Piqueres, Pablo Martinez-Gonzalez, Eugenio Aguirre, Miguel Garcia-Silviente, Marcelo Garcia-Perez, Jose M. Cañas, Francisco Martin-Rico, Jonathan Gines, Francisco Rivas-Montero, COMBAHO: A deep learning system for integrating brain injury patients in society, Pattern Recognition Letters, Volume 137, 2020, Pages 80-90, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2019.02.013.
Patrocinador
Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds; University of Alicante project GRE16-19; Valencian Government GV/2018/022; Spanish grants for PhD studies FPU15/04516 and ACIF/2017/243Resumen
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.
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