PHAROS 2.0—A PHysical Assistant RObot System Improved

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10045/98047
Información del item - Informació de l'item - Item information
Título: PHAROS 2.0—A PHysical Assistant RObot System Improved
Autor/es: Martinez-Martin, Ester | Costa, Angelo | Cazorla, Miguel
Grupo/s de investigación o GITE: Robótica y Visión Tridimensional (RoViT)
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Palabras clave: Assistive robotics | Active ageing | Decision support system | Cognitive assistant | Deep learning
Área/s de conocimiento: Ciencia de la Computación e Inteligencia Artificial
Fecha de publicación: 18-oct-2019
Editor: MDPI
Cita bibliográfica: Martinez-Martin E, Costa A, Cazorla M. PHAROS 2.0—A PHysical Assistant RObot System Improved. Sensors. 2019; 19(20):4531. doi:10.3390/s19204531
Resumen: There are great physical and cognitive benefits for older adults who are engaged in active aging, a process that should involve daily exercise. In our previous work on the PHysical Assistant RObot System (PHAROS), we developed a system that proposed and monitored physical activities. The system used a social robot to analyse, by means of computer vision, the exercise a person was doing. Then, a recommender system analysed the exercise performed and indicated what exercise to perform next. However, the system needed certain improvements. On the one hand, the vision system captured the movement of the person and indicated whether the exercise had been done correctly or not. On the other hand, the recommender system was based purely on a ranking system that did not take into account temporal evolution and preferences. In this work, we propose an evolution of PHAROS, PHAROS 2.0, incorporating improvements in both of the previously mentioned aspects. In the motion capture aspect, we are now able to indicate the degree of completeness of each exercise, identifying the part that has not been done correctly, and a real-time performance correction. In this way, the recommender system receives a greater amount of information and so can more accurately indicate the exercise to be performed. In terms of the recommender system, an algorithm was developed to weigh the performance, temporal evolution and preferences, providing a more accurate recommendation, as well as expanding the recommendation to a batch of exercises, instead of just one.
Patrocinador/es: This work was partly supported by the FCT—Fundação para a Ciência e Tecnología through the Post-Doc scholarship SFRH/BPD/102696/2014 and by the Spanish Government TIN2016-76515-R Grant supported with Feder funds.
URI: http://hdl.handle.net/10045/98047
ISSN: 1424-8220
DOI: 10.3390/s19204531
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Revisión científica: si
Versión del editor: https://doi.org/10.3390/s19204531
Aparece en las colecciones:INV - RoViT - Artículos de Revistas

Archivos en este ítem:
Archivos en este ítem:
Archivo Descripción TamañoFormato 
Thumbnail2019_Martinez-Martin_etal_Sensors.pdf12,88 MBAdobe PDFAbrir Vista previa


Este ítem está licenciado bajo Licencia Creative Commons Creative Commons