DeustoTeka Examinando por Autor "Casado Mansilla, Diego"

Examinando por Autor "Casado Mansilla, Diego"

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Addressing behavioural technologies through the human factor: a review
(Institute of Electrical and Electronics Engineers Inc., 2020-03-25) Irizar Arrieta, Ane; Gómez Carmona, Oihane; Bilbao Jayo, Aritz; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego; Almeida, Aitor
Energy-efficiency related research has reached a growing interest in recent years due to the imminent scarcity of non-renewable resources in our environment and the impending impacts their usage have on our environment. Thus, facing the reduction of energy waste and management has become a pivotal issue in our society. To cope with energy inefficiency, the scientific research community has identified the promotion of people's behaviour change as a critical field to foster environmental sustainability. However, the body of literature shows a lack of systematic methods and processes to reach a common ground when designing technology for promoting sustainable behaviour change. Therefore, this paper contributes with a thorough review and analysis of state of the art. Firstly, theoretical works related to behaviour change are collected and studied to clarify their main concepts and theories. Secondly, the different technologies, processes, methods and techniques applied in the field are reviewed to find diverse strategies in the application of the previously explained theoretical domains. Moreover, a wide range of systems developed to improve energy efficiency through human behaviour change is analysed (from augmented objects to the Internet of Things, digital applications or websites). Finally, the detected research gaps are listed to guide future research when aiming to raise the awareness of individuals through Information and Communication Technologies.
Analysis of driver's reaction behavior using a persuasion-based IT artefact
(MDPI, 2020-08-24) Goikoetxea González, Javier; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego
The use of interactive technology to change behavior, which is commonly known as persuasive technology, is currently gaining attention in information systems research. It has been assessed in many application domains and the field of private mobility is not an exception, notably with the advent of self-driven cars. However, the reviewed body of research shows that when it comes to linking persuasion-based systems and mobility, most of the approaches focus on engaging drivers to use the car in a safer way, leaving the cost-efficiency aspect of driving less explored. Therefore, this article focuses on the study of a persuasion-based IT (Information Technology) artefact devised to make drivers more aware of car expenses (e.g., maintenance control, engine failures, enhance driving, etc.). Specifically, it aims to identify persuasive design principles for a smart IT solution that is tailored for the enhancement of the cost-efficiency of private cars. To this purpose, the results of a survey, where respondents (N = 301) were asked to rank different principles of persuasion which might result in increased efficiency to save time and money within their car, are presented. This work aims to contribute a persuasion-based IT artefact to help and influence drivers, enhancing their management of costs related to car mobility in real-time. The implications of the proposed solution, according to the responses of the survey, are discussed in line with its implementation and adoption by car holders.
Blockchain application in P2P energy markets: social and legal aspects
(Taylor and Francis Ltd., 2022-03-08) Borges Hernández, Cruz E. ; Kapassa, Evgenia ; Touloupou, Marios ; Legarda Macon, Jon ; Casado Mansilla, Diego
Flexible and distributed energy markets are a reality that is progressively reaching many regions. Despite their clear benefits, they should be accepted by the prosumers. Additionally, blockchain technology and smart contracts have been characterised as a technological enabler for the energy sector and P2P Energy Markets (PEM). However, little research has been done to explore blockchain's user-centred perspective. Therefore, this paper analyses the reluctance and/or concerns of prosumers regarding smart contracts, and investigates their perception on blockchain within PEMs. The authors present the results of a survey conducted across several European countries addressing the implementation of automated trading systems and analysing the adoption of smart contracts. Considering that the main survey outcomes are related to the regulation and legislation uncertainty around blockchain usage, this paper explores also the fit of smart contracts from a legal perspective. Additionally, a set of recommendations to be used as the basis for the design and development of PEMs is delivered, aiming to adopt blockchain and smart contracts. As a key take-away, the authors confirm the crucial role that blockchain will play in the deployment of fair, secure, flexible and distributed energy markets by ensuring transparency in the exchange of information between prosumers and energy stakeholders.
A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildings
(Public Library of Science, 2024-05) Kalamaras, Ilias; Sánchez Corcuera, Rubén; Casado Mansilla, Diego; Tsolakis, Apostolos C.; Gómez Carmona, Oihane; Krinidis, Stelios; Borges Hernández, Cruz E.; Tzovaras, Dimitrios; López de Ipiña González de Artaza, Diego
Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees' decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings.
Climate change from B to Z: a cross-generational perception study in Spain
(Frontiers Media SA, 2025) Divasson Jaureguibarria, Asier; Aguayo Mendoza, Armando; Quesada Granja, Carlos; Casado Mansilla, Diego; Borges Hernández, Cruz E.
In the context of increasing climate concerns, this study explores generational perceptions and responses to potential climate-induced crises through a workshop and survey methodology. The aim of this study is to understand how different age groups view and react to extreme climate scenarios and evaluate their proposed actions and attitudes toward climate change mitigation. This study investigates generational perceptions and responses to climate change through a dual-format workshop and survey, conducted both in person and online. The methodological approach involved presenting respondents with a range of apocalyptic scenarios resulting from climate change, including electricity shortages, reduced food production, fuel scarcity, inadequate home heating, drought, and raw material shortages. These scenarios aimed to assess respondents’ awareness, concern, and proposed actions in response to potential future crises. The survey, administered via the Prolific platform, and workshops, held at the “Zientzia Azoka” science fair and online, gathered data from 153 participants across four generational cohorts, namely, Baby Boomers, Generation X (Gen X), Millennials, and Generation Z (Gen Z). The analysis revealed distinct generational differences in attitudes toward societal responsibility and action. Baby Boomers emphasized community responsibility over formal regulations, showing a preference for moral and ethical accountability rather than legislative action. Generation X displayed balanced responses, with tendencies toward valuing education and long-term stability. Millennials were more likely to emphasize the role of authorities and formal governance in addressing societal issues, reflecting their reliance on structured systems. In contrast, Generation Z showed a strong inclination to hold companies accountable, often associating responsibility with corporate entities, and were more vocal about behavioral changes and restrictions to drive progress. The study underscores significant generational differences in climate change perceptions and actions, highlighting a trend toward increasing demand for climate action and growing distrust in institutions. These insights suggest the need for inclusive, generationally tailored climate policies with a focus on education and systemic change. Future research should explore the relationship between sustainable consumption and economic vulnerability, addressing how financial constraints impact individuals’ ability to adopt sustainable practices
Cross-generational perceptions on climate change: resolutive insights from youth and elder
(Universidad del País Vasco = Euskal Herriko Unibertsitatea, 2024) Divasson Jaureguibarria, Asier; Quesada Granja, Carlos; Casado Mansilla, Diego; Cubillo Eguizabal, Iker; Aguayo Mendoza, Armando; Borges Hernández, Cruz E.
In a world increasingly dominated by conversations about climate change, this research delves deeper, exploring the disconnect between widespread awareness and the implementation of concrete actions to mitigate global warming and adapt to the energy transition. The study investigates this gap by focusing on the concerns of two distinct demographic groups: teenagers and adults. Conceived during the Zientzia Azoka event and developed through other events and sessions, the project actively engaged over 131 participants in a series of workshops. These participants spanned various age groups: 16 individuals under the age of 18, 110 adults between 18 and 65 years old, and 5 adults over 65 years old. The workshops employed qualitative methods, presenting participants with a range of potential climate crisis scenarios encompassing environmental challenges, energy shortages, and mobility restrictions. These scenarios explored situations such as insufficient renewable energy development leading to electricity shortages, extreme weather events causing food scarcity, and disruptions to travel due to fuel shortages. Participants’ responses to these scenarios were then subjected to a process of semi-quantification, enabling a more nuanced analysis of their concerns. The analysis revealed not only a clear awareness of these impending challenges among both teenagers and adults, but also a recognition of the substantial barriers hindering proactive solutions. These barriers encompassed economic constraints, a perceived lack of general awareness about the gravity of the situation, and the ever-evolving social landscape shaped by recent global events like the COVID-19 pandemic and the war in Ukraine. However, amidst these anxieties, a glimmer of hope emerged. Participants identified a potential shift in societal behavior, possibly driven by these very crises. Thematic analysis of their responses revealed a strong emphasis on the crucial role of sufficiency in mitigating climate change. This highlights the importance of reducing consumption and waste rather than solely relying on technological advancements as the solution. Additionally, peer influence was recognized as a significant force in shaping attitudes and behaviors, suggesting a powerful avenue for promoting positive change. The political dimension of climate action also came into sharp focus. Participants demonstrated a sophisticated understanding of the complexities surrounding political processes and the challenges they present. They emphasized the need for clear and effective communication from political leaders, while acknowledging concerns about political motivations and the influence of special interest groups. This underscores the intricate relationship between politics, media, and public perception, highlighting the need for a multi-faceted approach to climate communication.
A data-driven methodology for deriving electricity consumption typologies from smart meters
(Elsevier Ltd, 2025-09-22) Quesada Granja, Carlos; Montero Manso, Pablo; Pflugradt, Noah; Astigarraga, Leire ; Merveille, Chris ; Casado Mansilla, Diego; Borges Hernández, Cruz E.
We present a data-driven methodology to identify residential electricity consumption typologies from large-scale smart meter data. The proposed approach combines seasonal feature extraction, clustering via Self-Organizing Maps (SOMs), and expert-in-the-loop validation to ensure both statistical robustness and operational relevance. Although the methodology is tailored to residential consumption, it also captures other non-residential load patterns – such as commercial, industrial, and public-sector profiles – present in the analyzed data. The methodology was applied to more than 23,000 time series from five international datasets, resulting in 40 distinct consumption patterns. These clusters were grouped into five behavioral categories – primary residences, holiday homes, equipment-intensive households, offices, and public lighting – each capturing characteristic daily and seasonal load signatures. The resulting typology enables a more realistic and interpretable representation of household electricity use, improving the design of demand-side strategies, tariff schemes, and forecasting models. In particular, it offers a practical foundation for energy planning and policy targeting across diverse regions, and can inform real-time classification tools or adaptive services under conditions of data scarcity or external disruptions.
Demand forecasting tool for inventory control smart systems
(Croatian Communications and Information Society, 2021-06-09) Benhamida, Fatima Zohra; Kaddouri, Ouahiba; Ouhrouche, Tahar; Benaichouche, Mohammed; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego
With the availability of data and the increasing capabilities of data processing tools, many businesses are leveraging historical sales and demand data to implement smart inventory management systems. Demand forecasting is the process of estimating the consumption of products or services for future time periods. It plays an important role in the field of inventory control and Supply Chain, since it enables production and supply planning and therefore can reduce delivery times and optimize Supply Chain decisions. This paper presents an extensive literature review about demand forecasting methods for time-series data. Based on analysis results and findings, a new demand forecasting tool for inventory control is proposed. First, a forecasting pipeline is designed to allow selecting the most accurate demand forecasting method. The validation of the proposed solution is executed on Stock&Buy case study, a growing online retail platform. For this reason, two new methods are proposed: (1) a hybrid method, Comb-TSB, is proposed for intermittent and lumpy demand patterns. CombTSB automatically selects the most accurate model among a set of methods. (2) a clustering-based approach (ClustAvg) is proposed to forecast demand for new products which have very few or no sales history data. The evaluation process showed that the proposed tool achieves good forecasting accuracy by making the most appropriate choice while defining the forecasting method to apply for each product selection.
EngageMate advancing classroom interaction with an intelligent IoT assistant driven by the digital twin concept
(Universidad de Deusto = Deustuko Unibertsitatea, Servicio de Publicaciones = Argitalpen Zerbitzua, 2024) Casado Mansilla, Diego; Solé Palacín, Francesc Xavier; Gómez Carmona, Oihane; Navarro, Joan; López de Ipiña González de Artaza, Diego
The rapid evolution of educational technologies brings the Internet of Things (IoT) to theforefront as a transformative force, poised to reshape conventional teaching and learningmethods through its network of interconnected devices and systems. This paper introducesan innovative IoT device enhanced by digital twin technology, aimed at improving teachingefficacy by enabling interactions with virtual replicas of physical objects for immersive, real-timelearning simulations. The device goes beyond typical educational tools by monitoring classroomenvironmental conditions and assessing student engagement, supported by a web platform foreducators to evaluate their teaching performances. A practical application involving two uni-versity teachers demonstrated the device’s potential to significantly improve teaching strategiesand student engagement, though it also revealed challenges in integrating such technologiesinto educational settings. These findings emphasize the need for ongoing research and devel-opment to overcome barriers and fully exploit IoT and digital twin technologies in education,thus facilitating a more interactive, effective, and personalized learning environment.
Engagement and accessibility tools for pro-environmental action on air quality: the SOCIO-BEE paradigm
(Springer Science and Business Media Deutschland GmbH, 2024-01) Atutxa Ordeñana, Ekhi; García Torres, Sofía; Kyfonidis, Charalampos; Karanassos, Dimitrios; Kopsacheilis, Evangelos; Tsita, Christina; Casado Mansilla, Diego; Emvoliadis, Alexandros; Angelis, Georgios; López de Ipiña González de Artaza, Diego; Puerta Beldarrain, Maite; Drosou, Anastasios; Tzovaras, Dimitrios
The involvement of citizens and all stakeholders is crucial in tackling environmental and social matters. This, addressing equity and diversity issues, although challenging, is a necessary condition for achieving positive outcomes and ensuring that no one is left behind. To help ease this challenge, this work presents a systematic approach to ensure inclusive participation and leverage non-technical and technical elements to maximise stakeholder engagement in scientific activities to successfully address sustainability concerns. For that, it builds on the interim results of the H2020 SOCIO-BEE project, a Citizen science (CS) proposal to reduce air pollution through inclusive community engagement and social innovation. As part of an interdisciplinary CS project, an abductive systematic combining methodology was employed, which allowed for dialogue and collaboration between theory and practice throughout the whole process, during which separate groups of experts and potential end-users were involved. The article presents (i) the stakeholder engagement strategy codified in the SOCIO-BEE toolkit as a robust, actionable and inclusive foundation of engagement to CS activities; and (ii) the digital platform UX that allows setting up campaigns for measurements and assignment to citizens, incorporating the requirements for flexibility, accessibility, limited digital literacy, inclusion and legal and ethical considerations. Their combination and mutual interaction aim to leverage the pros of CS and technology whilst reducing their cons to ensure the four pillars of applicability, scalability, actionability, and inclusion. This is supported by the presented hybrid model which combines physical and virtual spaces and individual and collective action.
Exploring the application of the FOX model to foster pro-environmental behaviours in smart environments
(MDPI AG, 2020-08-14) Irizar Arrieta, Ane; Casado Mansilla, Diego; Retegi Uria, Aiur; Laschke, Matthias; López de Ipiña González de Artaza, Diego
The heterogeneity and dynamism of people make addressing user diversity and its categorisation critical factors, which should be carefully considered when developing pro-environmental strategies and interventions. Nevertheless, the complexities of individuals complicates the creation of modelling and classification systems. The aforementioned issue opens a research opportunity, which should be tackled to improve the development of human-centric systems and processes. Throughout the present piece of research, our objective is to bridge that gap by extracting knowledge and insights relating to how to address user diversity when designing technologies considering sustainable behaviour. For this, we explore the possibilities of the FOX model—an early meta-model to approach the diversity of individuals when addressing pro-environmental behaviour—to classify and understand individuals while taking their heterogeneity into account. After introducing the model, a qualitative survey of eight experts is conducted. From this study, relevant findings are analysed and exposed. Taking into account the gathered knowledge, three user profiles are developed, based on the dimensions proposed by the model. Furthermore, scenarios are created for each profile, presenting three case studies where different application modes of the model are described (personalised interventions, prediction and forecasting, and individual and collective interventions). Finally, the extracted findings are analysed, discussing the main issues related to the development of pro-environmental technologies and systems.
Exploring the computational cost of machine learning at the edge for human-centric Internet of Things
(Elsevier B.V., 2020-11) Gómez Carmona, Oihane; Casado Mansilla, Diego; Kraemer, Frank Alexander; López de Ipiña González de Artaza, Diego; García-Zubía, Javier
In response to users’ demand for privacy, trust and control over their data, executing machine learning tasks at the edge of the system has the potential to make the Internet of Things (IoT) applications and services more human-centric. This implies moving complex computation to a local stage, where edge devices must balance the computational cost of the machine learning techniques to meet the available resources. Thus, in this paper, we analyze all the factors affecting the classification process and empirically evaluate their impact in terms of performance and cost. We put the focus on Human Activity Recognition (HAR) systems, which represent a standard type of classification problems in human-centered IoT applications. We present a holistic optimization approach through input data reduction and feature engineering that aims to enhance all the stages of the classification pipeline and integrate both inference and training at the edge. The results of the conducted evaluation show that there is a highly non-linear trade-off to make between the computational cost, in terms of processing time, and the achieved classification accuracy. In the presented case of study, the computational effort can be reduced by 80% assuming a decline of the classification accuracy of only 3%. The potential impact of the optimization strategy highlights the importance of understanding the initial data and studying the most relevant characteristics of the signal to meet the cost–accuracy requirements. This would contribute to bringing embedded machine learning to the edge and, hence, creating spaces where human and machine intelligence could collaborate.
Fostering multi-stakeholder collaboration through co-production and rewarding
(Springer Science and Business Media Deutschland GmbH, 2023) López de Ipiña González de Artaza, Diego ; Badiola Martínez, Julen; Lauzurica, Daniel; Silva Palacios, Daniel ; Carballedo Morillo, Roberto ; Casado Mansilla, Diego ; Not, Elena; Leonardi, Chiara ; Misikangas, Pauli
This paper describes a digital tool to foster sustainable engagement of stakeholders in collaborative processes, namely Collaborative Environment. A distinguishing aspect of the tool is its co-production model-based project management and its emphasis on reusability. The paper also reasons on how this tool should be effectively complemented to realize long-run and effective stakeholder collaboration, which is essential in bringing about social innovation.
Human-in-the-loop machine learning: reconceptualizing the role of the user in interactive approaches
(Elsevier B.V., 2024-04) Gómez Carmona, Oihane; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego; García-Zubía, Javier
The rise of intelligent systems and smart spaces has opened up new opportunities for human–machine collaborations. Interactive Machine Learning (IML) contribute to fostering such collaborations. Nonetheless, IML solutions tend to overlook critical factors such as the timing, frequency and workload that drive this interaction and are vital to adapting these systems to users’ goals and engagement. To address this gap, this work explores users’ expectations towards IML solutions in the context of an interactive hydration monitoring system for the workplace, which represents a challenging environment to implement intelligent solutions that can collaborate with individuals. The proposed system involves users in the learning process by providing feedback on the success of detecting their drinking gestures and enabling them to contribute with additional examples of their data. A qualitative study was conducted to evaluate this use case, where participants completed specific tasks with varying levels of involvement. This study provides promising insights into the potential of placing the Human-in-the-Loop (HitL) to adapt and reconceptualize the users’ role in interactive solutions, highlighting the importance of considering human factors in designing more effective and flexible collaborative systems between humans and machines.
Humanized computing for higher collaboration and reciprocal learning between machines and people
(Sociedad de Estudios Vascos = Eusko Ikaskuntza, 2023) López de Ipiña González de Artaza, Diego; Casado Mansilla, Diego; Puerta Beldarrain, Maite; Gómez Carmona, Oihane
Lan honek bi ikuspegi soziotekniko aztertzen ditu teknologia digitalak erabiltzen dituzten komunitate adimendunak egiteko, makinen eta pertsonen arteko elkarrekiko ikaskuntza ahalbidetuko duen lankidetza handiagoa lortzeko. Alde batetik, koprodukzioak jokabide-aldaketa sustatzen du, herritarrak kodiseinuko eta baterako diseinuko prozesuan ahaldunduz, erabiltzailean oinarritutako irtenbideak diseinatuz, tokiko ezagutza aprobetxatuz, lankidetza sustatuz eta gaitasunen garapena erraztuz. Bestalde, herritarren zientziak jokabide-aldaketa eragin eta ahalbidetu dezake, komunitatearentzako ekintza jasangarriagoak, arduratsuagoak eta orientatuagoak egiteko, kontzientziazioa, komunitatearen gaikuntza eta lankidetza sustatuz. Lan honen arabera, bi ikuspegi horiek lagun diezagukete aurrera egiten, gizakiak beti begiztan sartuko dituen informatika-belaunaldi berri baterantz, hau da, informatika humanizaturantz.
An image-based sensor system for low-cost airborne particle detection in citizen science air quality monitoring
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-10) Shah, Syed Mohsin Ali; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego
Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people’s exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of (Formula presented.) particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for (Formula presented.) particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.
Mind the gap: the AURORAL ecosystem for the digital transformation of smart communities and rural areas
(Elsevier Ltd, 2023-08) Gómez Carmona, Oihane; Buján Carballal, David; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego; Cano de Benito, Juan; Cimmino, Andrea; Poveda Villalón, María; García Castro, Raúl; Almela Miralles, Jorge; Apostolidis, Dimitris; Drosou, Anastasios; Tzovaras, Dimitrios; Wagner, Martin; Guadalupe Rodríguez, María; Salinas, Diego; Esteller, David; Riera Rovira, Martí; González, Arnau; Clavijo Ágreda, Jaime; Díez Frias, Alberto; Bocanegra Yáñez, María del Carmen; Pedro Henriques, Rui; Ferreira Nunes, Elsa; Lux, Marian; Bujalkova, Nikol
Rural areas play a crucial role in addressing challenges related to climate change, food provision, biomass, and energy. At the same time, digital solutions have proven essential in improving safety, quality of life, and resilience in daily life. However, the lower population density and the lack of digital infrastructure in such rural areas make it difficult to develop technology-driven private businesses and public services. This can negatively impact socio-economic indicators and hinder the development of new services to cover peoples’ needs. For this reason, in this document, we seek to provide a stronger focus on rural regions in digitalization efforts and create new opportunities for rural communities. For that, we analyze the barriers and needs of the rural environment and present AURORAL, a digital service platform designed to meet the needs and contexts of rural areas. This ecosystem, comprising sustainable and multi-interoperable apps and services, can help communities succeed in innovation and smart transformation, providing the necessary infrastructure to facilitate long-lasting social, environmental, and economic benefits by prioritizing openness, interoperability, and decentralization. On the principle that the full potential of these technologies can only be realized when they are integrated into societal and economic activity and organization, AURORAL aims to promote economic growth and digitalization in the rural domain and contribute to bridging the digital divide between rural and urban areas.
A multifaceted vision of the human-AI collaboration: a comprehensive review
(Institute of Electrical and Electronics Engineers Inc., 2025) Puerta Beldarrain, Maite; Gómez Carmona, Oihane; Sánchez Corcuera, Rubén; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego; Chen, Liming
Human-AI collaboration has evolved into a complex, multidimensional paradigm shaped by research in various domains. Key areas such as human-in-The-loop systems, Interactive Machine Learning (IML), Hybrid Intelligence, and Human-Agent Interaction have significantly contributed to this development. However, these fields often lack cohesion, underscoring the need for a cohesive perspective to advance. This work addresses this gap by integrating insights from diverse aspects of collaboration to present a holistic approach to fostering effective and adaptive interactions between humans and artificial agents. It emphasizes empowering end-users with greater control and involvement in decision-making processes, thereby enhancing both the levels of interactivity and adaptability within intelligent systems. Moving beyond a focus on AI training techniques, this paper presents a broader perspective on incorporating human input into AI decision-making and learning processes, highlighting the importance of flexibility in systems and user engagement. The manuscript proposes a framework encompassing five levels of human integration and examines their relationship with core collaboration aspects, including the system purpose, participant expertise, and system proactivity. By synthesizing current knowledge on human-AI collaboration and outlining essential design principles, this work aims to advance the field and foster interdisciplinary collaboration among researchers, practitioners, and designers
Opportunities and challenges of technology-Based interventions to increase health-wareness in the workplace
(IOS Press, 2019) Gómez Carmona, Oihane; Casado Mansilla, Diego; García-Zubía, Javier
Well-being at work is gaining an increasing importance on the overall health promotion as the workplace is considered an adequate setting to support health-related interventions reaching large audiences. In fact, an increasing number of initiatives are being carried out to influence employees towards healthier lifestyles in later years. However, despite demonstrating moderate efficacy, the body of literature shows that the lack of adherence of the target audience to the interventions is an important factor to overcome in order to attain higher success. To increase employees' motivation and prevent early drop-out, disengagement or high attrition rates, this work presents an intervention methodology based on the Internet of Things (IoT) paradigm. Specifically, it presents a novel concept of a participatory workercentric IoT solution for enhancing individuals' well-being in office environments. This approach seeks to stress the significance of empowering workers providing to them fine-grained control of their own well-being and self-care which correlates to higher rates of participation in health promotion initiatives. Along this chapter the main challenges associated with the design and development of technology-based interventions are reviewed. Moreover, the value of increasing the acceptance and adoption of the presented IoT approach from the employee's perspective is analyzed in a comprehensive manner.
Optimizing computational resources for edge intelligence through model cascade strategies
(Institute of Electrical and Electronics Engineers Inc., 2022-05-15) Gómez Carmona, Oihane; Casado Mansilla, Diego; López de Ipiña González de Artaza, Diego; García-Zubía, Javier
As the number of interconnected devices increases and more artificial intelligence (AI) applications upon the Internet of Things (IoT) start to flourish, so does the environmental cost of the computational resources needed to send and process all the generated data. Therefore, promoting the optimization of AI applications is a key factor for the sustainable development of IoT solutions. Paradigms such as Edge Computing are progressively proposed as a solution in the IoT field, becoming an alternative to delegate all the computation to the Cloud. However, bringing the computation to the local stage is limited by the resources' availability of the devices hosted at the Edge of the network. For this reason, this work presents an approach that simplifies the complexity of supervised learning algorithms at the Edge. Specifically, it separates complex models into multiple simpler classifiers forming a cascade of discriminative models. The suitability of this proposal in a human activity recognition (HAR) context is assessed by comparing the performance of three different variations of this strategy. Furthermore, its computational cost is analyzed in several resource-constrained Edge devices in terms of processing time. The experimental results show the viability of this approach to outperform other ensemble methods, i.e., the Stacking technique. Moreover, it substantially reduces the computational cost of the classification tasks by more than 60% without a significant accuracy loss (around 3.5%). This highlights the potential of this strategy to reduce resource and energy requirements in IoT architectures and promote more efficient and sustainable classification solutions.

Examinando por Autor "Casado Mansilla, Diego"

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