Authors:
Jordi Torres
1
;
Meritxell Garcia
1
;
Garazi Artola
1
;
2
;
Teresa Garcia-Navarro
1
;
Isabel Amaya
1
;
Nekane Larburu
1
;
2
and
Cristina Martin
1
;
3
Affiliations:
1
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain
;
2
Biodonostia Health Research Institute (Bioengineering Area), eHealth Group, 20014 Doonstia-San Sebastián, Spain
;
3
Faculty of Engineering, University of Deusto, Av.Universidades, 24, 48008, Bilbao, Spain
Keyword(s):
Healthy Aging, Recommender System, Quality of Life, Synthetic Data Generation.
Abstract:
The needs of the currently aging population require new technologies to support them in order to offer them a decent quality of life. Different interventions have been proposed in the last years to face this challenge, where recommender systems are gaining strength. The general objective of these systems is to promote the adoption of healthy habits among the end users, but sometimes they show limitations in the fulfilment of this goal. To overcome these limitations, our approach offers an easy to maintain, interoperable, and personalized recommender system capable of providing recommendations based on individuals’ daily activity data. A methodology is presented for the generation and management of wellbeing recommendations, which are then tested using a synthetically generated dataset that simulates a variety of user categories. With the evaluation of this data, a technical validation is carried on to assess the performance and scalability of our developed system.