PHAROS 2.0—A PHysical Assistant RObot System Improved
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Título: | PHAROS 2.0—A PHysical Assistant RObot System Improved |
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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 |
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