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Article

Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities

1
ETSII (Escuela Técnica Superior de Ingeniería Industrial), Technical University of Cartagena, St. Dr. Fleming, s/n, 30203 Cartagena, Spain
2
AASS (Applied Autonomous Sensor Systems), Örebro University, 70281 Örebro, Sweden
3
The Hamlyn Centre for Robotic Surgery, Imperial College London, London SW7 2AZ, UK
4
Department of Evolutionary and Educational Psychology, Faculty of Psychology, Campus Regional Excellence Mare Nostrum, University of Murcia, 30100 Murcia, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Susanna Spinsante, Francisco Florez-Revuelta, Wiktoria Wilkowska and Pau Climent-Perez
Sensors 2021, 21(20), 6865; https://doi.org/10.3390/s21206865
Received: 8 September 2021 / Revised: 6 October 2021 / Accepted: 12 October 2021 / Published: 16 October 2021
(This article belongs to the Special Issue Sensor and Assistive Technologies for Smart Life)
The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot’s autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought. View Full-Text
Keywords: assistive robotics; affective computing; ambient assisted living; smart home; mood prediction; mental well-being; quality of life; ecological momentary assessment (EMA); machine learning; ROS assistive robotics; affective computing; ambient assisted living; smart home; mood prediction; mental well-being; quality of life; ecological momentary assessment (EMA); machine learning; ROS
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MDPI and ACS Style

Calatrava-Nicolás, F.M.; Gutiérrez-Maestro, E.; Bautista-Salinas, D.; Ortiz, F.J.; González, J.R.; Vera-Repullo, J.A.; Jiménez-Buendía, M.; Méndez, I.; Ruiz-Esteban, C.; Mozos, O.M. Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities. Sensors 2021, 21, 6865. https://doi.org/10.3390/s21206865

AMA Style

Calatrava-Nicolás FM, Gutiérrez-Maestro E, Bautista-Salinas D, Ortiz FJ, González JR, Vera-Repullo JA, Jiménez-Buendía M, Méndez I, Ruiz-Esteban C, Mozos OM. Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities. Sensors. 2021; 21(20):6865. https://doi.org/10.3390/s21206865

Chicago/Turabian Style

Calatrava-Nicolás, Francisco M., Eduardo Gutiérrez-Maestro, Daniel Bautista-Salinas, Francisco J. Ortiz, Joaquín R. González, José A. Vera-Repullo, Manuel Jiménez-Buendía, Inmaculada Méndez, Cecilia Ruiz-Esteban, and Oscar M. Mozos 2021. "Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities" Sensors 21, no. 20: 6865. https://doi.org/10.3390/s21206865

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