A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildings.

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.

Development of Continuous Assessment of Muscle Quality and Frailty in Older Patients Using Multiparametric Combinations of Ultrasound and Blood Biomarkers: Protocol for the ECOFRAIL Study.

BACKGROUND: Frailty resulting from the loss of muscle quality can potentially be delayed through early detection and physical exercise interventions. There is a demand for cost-effective tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessments. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional, and microstructural muscle properties, while biological assays derived from blood samples are associated with functional information. OBJECTIVE: This study aims to assess multiparametric combinations of ultrasound and blood-based biomarkers to offer a cross-sectional evaluation of the patient frailty phenotype and to track changes in muscle quality associated with supervised exercise programs. METHODS: This prospective observational multicenter study will include patients aged 70 years and older who are capable of providing informed consent. We aim to recruit 100 patients from hospital environments and 100 from primary care facilities. Each patient will undergo at least two examinations (baseline and follow-up), totaling a minimum of 400 examinations. In hospital environments, 50 patients will be measured before/after a 16-week individualized and supervised exercise program, while another 50 patients will be followed up after the same period without intervention. Primary care patients will undergo a 1-year follow-up evaluation. The primary objective is to compare cross-sectional evaluations of physical performance, functional capacity, body composition, and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based assays. We will analyze ultrasound raw data obtained with a point-of-care device, along with a set of biomarkers previously associated with frailty, using quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. Additionally, we will examine the sensitivity of these biomarkers to detect short-term muscle quality changes and functional improvement after a supervised exercise intervention compared with usual care. RESULTS: At the time of manuscript submission, the enrollment of volunteers is ongoing. Recruitment started on March 1, 2022, and ends on June 30, 2024. CONCLUSIONS: The outlined study protocol will integrate portable technologies, using quantitative muscle ultrasound and blood biomarkers, to facilitate an objective cross-sectional assessment of muscle quality in both hospital and primary care settings. The primary objective is to generate data that can be used to explore associations between biomarker combinations and the cross-sectional clinical assessment of frailty and sarcopenia. Additionally, the study aims to investigate musculoskeletal changes following multicomponent physical exercise programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT05294757; https://clinicaltrials.gov/ct2/show/NCT05294757. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50325.

Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation.

Data scientists spend much time with data cleaning tasks, and this is especially important when dealing with data gathered from sensors, as finding failures is not unusual (there is an abundance of research on anomaly detection in sensor data). This work analyzes several aspects of the data generated by different sensor types to understand particularities in the data, linking them with existing data mining methodologies. Using data from different sources, this work analyzes how the type of sensor used and its measurement units have an important impact in basic statistics such as variance and mean, because of the statistical distributions of the datasets. The work also analyzes the behavior of outliers, how to detect them, and how they affect the equivalence of sensors, as equivalence is used in many solutions for identifying anomalies. Based on the previous results, the article presents guidance on how to deal with data coming from sensors, in order to understand the characteristics of sensor datasets, and proposes a parallelized implementation. Finally, the article shows that the proposed decision-making processes work well with a new type of sensor and that parallelizing with several cores enables calculations to be executed up to four times faster.

PyFF: A Fog-Based Flexible Architecture for Enabling Privacy-by-Design IoT-Based Communal Smart Environments.

The advent of the Internet of Things (IoT) and the massive growth of devices connected to the Internet are reshaping modern societies. However, human lifestyles are not evolving at the same pace as technology, which often derives into users' reluctance and aversion. Although it is essential to consider user involvement/privacy while deploying IoT devices in a human-centric environment, current IoT architecture standards tend to neglect the degree of trust that humans require to adopt these technologies on a daily basis. In this regard, this paper proposes an architecture to enable privacy-by-design with human-in-the-loop IoT environments. In this regard, it first distills two IoT use-cases with high human interaction to analyze the interactions between human beings and IoT devices in an environment which had not previously been subject to the Internet of People principles.. Leveraging the lessons learned in these use-cases, the Privacy-enabling Fog-based and Flexible (PyFF) human-centric and human-aware architecture is proposed which brings together distributed and intelligent systems are brought together. PyFF aims to maintain end-users' privacy by involving them in the whole data lifecycle, allowing them to decide which information can be monitored, where it can be computed and the appropriate feedback channels in accordance with human-in-the-loop principles.

Exploring the Application of the FOX Model to Foster Pro-Environmental Behaviours in Smart Environments.

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.

Towards Citizen Co-Created Public Service Apps.

WeLive project's main objective is about transforming the current e-government approach by providing a new paradigm based on a new open model oriented towards the design, production and deployment of public services and mobile apps based on the collaboration of different stakeholders. These stakeholders form the quadruple helix, i.e., citizens, private companies, research institutes and public administrations. Through the application of open innovation, open data and open services paradigms, the framework developed within the WeLive project enables the co-creation of urban apps. In this paper, we extend the description of the WeLive platform presented at , plus the preliminary results of the first pilot phase. The two-phase evaluation methodology designed and the evaluation results of first pilot sub-phase are also presented.

Promotion of active ageing combining sensor and social network data.

The increase of life expectancy in modern society has caused an increase in elderly population. Elderly people want to live independently in their home environment for as long as possible. However, as we age, our physical skills tend to worsen and our social circle tends to become smaller, something that often leads to a considerable decrease of both our physical and social activities. In this paper, we present an AAL framework developed within the SONOPA project, whose objective is to promote active ageing by combining a social network with information inferred using in-home sensors.

Collaboration-Centred Cities through Urban Apps Based on Open and User-Generated Data.

This paper describes the IES Cities platform conceived to streamline the development of urban apps that combine heterogeneous datasets provided by diverse entities, namely, government, citizens, sensor infrastructure and other information data sources. This work pursues the challenge of achieving effective citizen collaboration by empowering them to prosume urban data across time. Particularly, this paper focuses on the query mapper; a key component of the IES Cities platform devised to democratize the development of open data-based mobile urban apps. This component allows developers not only to use available data, but also to contribute to existing datasets with the execution of SQL sentences. In addition, the component allows developers to create ad hoc storages for their applications, publishable as new datasets accessible by other consumers. As multiple users could be contributing and using a dataset, our solution also provides a data level permission mechanism to control how the platform manages the access to its datasets. We have evaluated the advantages brought forward by IES Cities from the developers' perspective by describing an exemplary urban app created on top of it. In addition, we include an evaluation of the main functionalities of the query mapper.

Real-Time Statistical Modeling of Blood Sugar.

Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient's historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.

Real-Time Personalized Monitoring to Estimate Occupational Heat Stress in Ambient Assisted Working.

Ambient Assisted Working (AAW) is a discipline aiming to provide comfort and safety in the workplace through customization and technology. Workers' comfort may be compromised in many labor situations, including those depending on environmental conditions, like extremely hot weather conduces to heat stress. Occupational heat stress (OHS) happens when a worker is in an uninterrupted physical activity and in a hot environment. OHS can produce strain on the body, which leads to discomfort and eventually to heat illness and even death. Related ISO standards contain methods to estimate OHS and to ensure the safety and health of workers, but they are subjective, impersonal, performed a posteriori and even invasive. This paper focuses on the design and development of real-time personalized monitoring for a more effective and objective estimation of OHS, taking into account the individual user profile, fusing data from environmental and unobtrusive body sensors. Formulas employed in this work were taken from different domains and joined in the method that we propose. It is based on calculations that enable continuous surveillance of physical activity performance in a comfortable and healthy manner. In this proposal, we found that OHS can be estimated by satisfying the following criteria: objective, personalized, in situ, in real time, just in time and in an unobtrusive way. This enables timely notice for workers to make decisions based on objective information to control OHS.

A novel software architecture for the provision of context-aware semantic transport information.

The effectiveness of Intelligent Transportation Systems depends largely on the ability to integrate information from diverse sources and the suitability of this information for the specific user. This paper describes a new approach for the management and exchange of this information, related to multimodal transportation. A novel software architecture is presented, with particular emphasis on the design of the data model and the enablement of services for information retrieval, thereby obtaining a semantic model for the representation of transport information. The publication of transport data as semantic information is established through the development of a Multimodal Transport Ontology (MTO) and the design of a distributed architecture allowing dynamic integration of transport data. The advantages afforded by the proposed system due to the use of Linked Open Data and a distributed architecture are stated, comparing it with other existing solutions. The adequacy of the information generated in regard to the specific user's context is also addressed. Finally, a working solution of a semantic trip planner using actual transport data and running on the proposed architecture is presented, as a demonstration and validation of the system.

Combining users' activity survey and simulators to evaluate human activity recognition systems.

Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

Presentation accuracy of the web revisited: animation methods in the HTML5 era.

Using the Web to run behavioural and social experiments quickly and efficiently has become increasingly popular in recent years, but there is some controversy about the suitability of using the Web for these objectives. Several studies have analysed the accuracy and precision of different web technologies in order to determine their limitations. This paper updates the extant evidence about presentation accuracy and precision of the Web and extends the study of the accuracy and precision in the presentation of multimedia stimuli to HTML5-based solutions, which were previously untested. The accuracy and precision in the presentation of visual content in classic web technologies is acceptable for use in online experiments, although some results suggest that these technologies should be used with caution in certain circumstances. Declarative animations based on CSS are the best alternative when animation intervals are above 50 milliseconds. The performance of procedural web technologies based on the HTML5 standard is similar to that of previous web technologies. These technologies are being progressively adopted by the scientific community and have promising futures, which makes their use advisable to utilizing more obsolete technologies.

Integration of multisensor hybrid reasoners to support personal autonomy in the smart home.

The deployment of the Ambient Intelligence (AmI) paradigm requires designing and integrating user-centered smart environments to assist people in their daily life activities. This research paper details an integration and validation of multiple heterogeneous sensors with hybrid reasoners that support decision making in order to monitor personal and environmental data at a smart home in a private way. The results innovate on knowledge-based platforms, distributed sensors, connected objects, accessibility and authentication methods to promote independent living for elderly people. TALISMAN+, the AmI framework deployed, integrates four subsystems in the smart home: (i) a mobile biomedical telemonitoring platform to provide elderly patients with continuous disease management; (ii) an integration middleware that allows context capture from heterogeneous sensors to program environment's reaction; (iii) a vision system for intelligent monitoring of daily activities in the home; and (iv) an ontologies-based integrated reasoning platform to trigger local actions and manage private information in the smart home. The framework was integrated in two real running environments, the UPM Accessible Digital Home and MetalTIC house, and successfully validated by five experts in home care, elderly people and personal autonomy.

Modeling users, context and devices for ambient assisted living environments.

The participation of users within AAL environments is increasing thanks to the capabilities of the current wearable devices. Furthermore, the significance of considering user's preferences, context conditions and device's capabilities help smart environments to personalize services and resources for them. Being aware of different characteristics of the entities participating in these situations is vital for reaching the main goals of the corresponding systems efficiently. To collect different information from these entities, it is necessary to design several formal models which help designers to organize and give some meaning to the gathered data. In this paper, we analyze several literature solutions for modeling users, context and devices considering different approaches in the Ambient Assisted Living domain. Besides, we remark different ongoing standardization works in this area. We also discuss the used techniques, modeled characteristics and the advantages and drawbacks of each approach to finally draw several conclusions about the reviewed works.

Measuring software timing errors in the presentation of visual stimuli in cognitive neuroscience experiments.

Because of the features provided by an abundance of specialized experimental software packages, personal computers have become prominent and powerful tools in cognitive research. Most of these programs have mechanisms to control the precision and accuracy with which visual stimuli are presented as well as the response times. However, external factors, often related to the technology used to display the visual information, can have a noticeable impact on the actual performance and may be easily overlooked by researchers. The aim of this study is to measure the precision and accuracy of the timing mechanisms of some of the most popular software packages used in a typical laboratory scenario in order to assess whether presentation times configured by researchers do not differ from measured times more than what is expected due to the hardware limitations. Despite the apparent precision and accuracy of the results, important issues related to timing setups in the presentation of visual stimuli were found, and they should be taken into account by researchers in their experiments.

Context management platform for tourism applications.

The notion of context has been widely studied and there are several authors that have proposed different definitions of context. However, context has not been widely studied in the framework of human mobility and the notion of context has been imported directly from other computing fields without specifically addressing the tourism domain requirements. In order to store and manage context information a context data model and a context management platform are needed. Ontologies have been widely used in context modelling, but many of them are designed to be applied in general ubiquitous computing environments, do not contain specific concepts related to the tourism domain or some approaches do not contain enough concepts to represent context information related to the visitor on the move. That is why we propose a new approach to provide a better solution to model context data in tourism environments, adding more value to our solution reusing data about tourist resources from an Open Data repository and publishing it as Linked Data. We also propose the architecture for a context information management platform based on this context data model.

Semantic framework for social robot self-configuration.

Healthcare environments, as many other real world environments, present many changing and unpredictable situations. In order to use a social robot in such an environment, the robot has to be prepared to deal with all the changing situations. This paper presents a robot self-configuration approach to overcome suitably the commented problems. The approach is based on the integration of a semantic framework, where a reasoner can take decisions about the configuration of robot services and resources. An ontology has been designed to model the robot and the relevant context information. Besides rules are used to encode human knowledge and serve as policies for the reasoner. The approach has been successfully implemented in a mobile robot, which showed to be more capable of solving situations not pre-designed.

Mobile monitoring and reasoning methods to prevent cardiovascular diseases.

With the recent technological advances, it is possible to monitor vital signs using Bluetooth-enabled biometric mobile devices such as smartphones, tablets or electric wristbands. In this manuscript, we present a system to estimate the risk of cardiovascular diseases in Ambient Assisted Living environments. Cardiovascular disease risk is obtained from the monitoring of the blood pressure by means of mobile devices in combination with other clinical factors, and applying reasoning techniques based on the Systematic Coronary Risk Evaluation Project charts. We have developed an end-to-end software application for patients and physicians and a rule-based reasoning engine. We have also proposed a conceptual module to integrate recommendations to patients in their daily activities based on information proactively inferred through reasoning techniques and context-awareness. To evaluate the platform, we carried out usability experiments and performance benchmarks.

A methodology and a web platform for the collaborative development of context-aware systems.

Information and services personalization is essential for an optimal user experience. Systems have to be able to acquire data about the user's context, process them in order to identify the user's situation and finally, adapt the functionality of the system to that situation, but the development of context-aware systems is complex. Data coming from distributed and heterogeneous sources have to be acquired, processed and managed. Several programming frameworks have been proposed in order to simplify the development of context-aware systems. These frameworks offer high-level application programming interfaces for programmers that complicate the involvement of domain experts in the development life-cycle. The participation of users that do not have programming skills but are experts in the application domain can speed up and improve the development process of these kinds of systems. Apart from that, there is a lack of methodologies to guide the development process. This article presents as main contributions, the implementation and evaluation of a web platform and a methodology to collaboratively develop context-aware systems by programmers and domain experts.