Next Article in Journal
A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals
Previous Article in Journal
An Improved Electronic Image Motion Compensation (IMC) Method of Aerial Full-Frame-Type Area Array CCD Camera Based on the CCD Multiphase Structure and Hardware Implementation
Previous Article in Special Issue
Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(8), 2633; https://doi.org/10.3390/s18082633

PHAROS—PHysical Assistant RObot System

1
ALGORITMI Center, University of Minho, 4704-553 Braga, Portugal
2
RoViT, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain
3
Departamento Sistemas Informáticos y Computación, Universitat Politècnica de València, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Received: 28 June 2018 / Revised: 3 August 2018 / Accepted: 8 August 2018 / Published: 11 August 2018
(This article belongs to the Special Issue Smart Decision-Making)
View Full-Text   |   Download PDF [3889 KB, uploaded 11 August 2018]   |  

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. View Full-Text
Keywords: robot assistant; deep learning; cognitive assistant; elderly physical exercise; human exercise recognition; gesture recognition; ambient assisted living robot assistant; deep learning; cognitive assistant; elderly physical exercise; human exercise recognition; gesture recognition; ambient assisted living
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Costa, A.; Martinez-Martin, E.; Cazorla, M.; Julian, V. PHAROS—PHysical Assistant RObot System. Sensors 2018, 18, 2633.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top