PHAROS—PHysical Assistant RObot System

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/78331
Información del item - Informació de l'item - Item information
Title: PHAROS—PHysical Assistant RObot System
Authors: Costa, Angelo | Martinez-Martin, Ester | Cazorla, Miguel | Julián Inglada, Vicente
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Robot assistant | Deep learning | Cognitive assistant | Elderly physical exercise | Human exercise recognition | Gesture recognition | Ambient assisted living
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 11-Aug-2018
Publisher: MDPI
Citation: Costa A, Martinez-Martin E, Cazorla M, Julian V. PHAROS—PHysical Assistant RObot System. Sensors. 2018; 18(8):2633. doi:10.3390/s18082633
Abstract: The great demographic change leading to an ageing society demands technological solutions to satisfy the increasing varied elderly needs. This paper presents PHAROS, an interactive robot system that recommends and monitors physical exercises designed for the elderly. The aim of PHAROS is to be a friendly elderly companion that periodically suggests personalised physical activities, promoting healthy living and active ageing. Here, it is presented the PHAROS architecture, components and experimental results. The architecture has three main strands: a Pepper robot, that interacts with the users and records their exercises performance; the Human Exercise Recognition, that uses the Pepper recorded information to classify the exercise performed using Deep Leaning methods; and the Recommender, a smart-decision maker that schedules periodically personalised physical exercises in the users’ agenda. The experimental results show a high accuracy in terms of detecting and classifying the physical exercises (97.35%) done by 7 persons. Furthermore, we have implemented a novel procedure of rating exercises on the recommendation algorithm. It closely follows the users’ health status (poor performance may reveal health problems) and adapts the suggestions to it. The history may be used to access the physical condition of the user, revealing underlying problems that may be impossible to see otherwise.
Sponsor: 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 TIN2016-6515-R and TIN2015-65515-C4-1-R Grants supported with Feder funds.
URI: http://hdl.handle.net/10045/78331
ISSN: 1424-8220
DOI: 10.3390/s18082633
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 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/).
Peer Review: si
Publisher version: https://doi.org/10.3390/s18082633
Appears in Collections:INV - RoViT - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2018_Costa_etal_Sensors.pdf3,9 MBAdobe PDFOpen Preview


This item is licensed under a Creative Commons License Creative Commons