Examinando por Autor "Espinilla, Macarena"
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Ítem A New Horizon in Healthcare: An Innovative Methodology for Sensor-Based Adherence Platforms in Home Monitoring of Key Treatment Indicators(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024-10) Díaz Jiménez, David; López Ruiz, Jose Luis; González Lama, Jesús; Espinilla, MacarenaÍtem Assessment of sustainable development objectives in Smart Labs: technology and sustainability at the service of society(ELSEVIER, 2021-11-06) Verdejo, Ángeles; Espinilla, Macarena; López Ruiz, Jose Luis; Francisco, JuradoSustainable development is the working basis of engineering research and cities are becoming increasingly flexible, inclusive and intelligent. In this context, there is a need for environments that emulate real-life spaces in which cutting-edge technologies can be implemented for subsequent deployment in society. Smart Labs or Living Labs are spaces for innovation, research and experimentation that integrate systems, devices and methodologies focused on people and their environments. The technologies studied and developed in such labs can then be deployed in human spaces to provide intelligence, comfort, health and sustainability. Health and wellness, energy and environment, artificial intelligence, big data and digital rights are some of the disciplines being studied. At the same time, the UN 2030 Agenda provides a comprehensive framework to promote human well-being through the Sustainable Development Goals. In this work, an evaluation model of its indicators in smart environments is performed through a mixed review methodology. The objective of this work is the analysis and implementation of the SDGs in Smart Labs through a literature review and a case study of UJAmI, the smart laboratory of the University of Ja´en. The results provide quantitative and qualitative data on the present and future of the smart devices implemented in the UJAmI lab, providing a roadmap for future developments.Ítem Evaluation of the Impact of the Sustainable Development Goals on anActivity Recognition Platform for Healthcare Systems(MDPI, 2023) López Ruiz, José Luis; Espinilla, Macarena; Verdejo, ÁngelesÍtem Transforming Elderly Care Through Ethical and Social Evaluation of Intelligent Activity Recognition Systems in Nursing Homes(Springer, 2023-11-30) Montoro Lendínez, Alicia; Linares, Carmen; Perandres, Ana; Cruz, Alfonso; López Ruiz, José Luis; Nugent, Chris; Espinilla, MacarenaÍtem Using Linguistic Incomplete Preference Relations to Cold start Recommendations(Emerald, 2010-06) Rodríguez, Rosa M.; Espinilla, Macarena; Sánchez, Pedro J.; Martínez, LuisPurpose – Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge-based recommendation model to provide good cold start recommendations. Design/methodology/approach – Hybridizing a collaborative system and a knowledge-based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers’ preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach. Findings – The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge-based recommender systems improves the performance in those situations when collaborative models do not work properly. Research limitations/implications – Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations. Practical implications – A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants. Originality/value – Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.