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Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines.

PurposeThe tourism and hospitality sectors are experiencing radical innovation boosted by the advancements in Information and Communication Technologies. Increasingly sophisticated chatbots are introducing novel approaches, re-shaping the dynamics among tourists and service providers, and fostering a remarkable behavioral change in the overall sector. Therefore, the objective of this paper is two-folded: (1) to highlight the academic and industrial standing points with respect to the current chatbots designed/deployed in the tourism sector and (2) to develop a proof-of-concept embodying the most prominent opportunities in the tourism sector.Design/methodology/approachThis work elaborates on the outcomes of a Systematic Literature Review (SLR) and a Focus Group (FG) composed of experts from the tourism industry. Moreover, it presents a proof-of-concept relying on the outcomes obtained from both SLR and FG. Eventually, the proof-of-concept has been tested with experts and practitioners of the tourism sector.FindingsAmong the findings elicited by this paper, we can mention the quick evolution of chatbot-based solutions, the need for continuous investments, upskilling, system innovation to tackle the eTourism challenges and the shift toward new dimensions (i.e. tourist-to-tourist-to-chatbot and personalized multi-stakeholder systems). In particular, we focus on the need for chatbot-based activity and thematic aggregation for next-generation tourists and service providers.Originality/valueBoth academic- and industrial-centered findings have been structured and discussed to foster the practitioners' future research. Moreover, the proof-of-concept presented in the paper is the first of its kind, which raised considerable interest from both technical and business-planning perspectives.

Early diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. Additionally, the correlation and dependence were calculated to examine the strength and direction of association between each extracted feature and each classification of the group. The average classification accuracies in 20 trials were 95% (SVM), 71.50% (RF), 82.58% (RF), 84.91% (SVM), 85.83% (RF), and 85.08% (RF), respectively, with the best accuracies being 100% (SVM, RF, and MLP), 83.33% (RF), 91.66% (RF), 95% (SVM, and MLP), 96.66% (RF), and 93.33% (DT). Cognitive scores, HC and LV area sizes, and HC texture features demonstrated significant potential for diagnosing AD and its subtypes for all groups. RF and SVM showed better performance in distinguishing between groups. These findings highlight the importance of using 2D-MRI to identify key features containing critical information for early diagnosis of AD.Article HighlightsCognitive scores, brain structure sizes, and tissue features can assist in diagnosing Alzheimer’s and its early stages.Machine learning models classify Alzheimer’s stages using optimized brain MRI features.MRI scans show how brain features change as Alzheimer’s progresses.

Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions –in the form of mobile apps and wearables– have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support.

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes.

RDF and SPARQL are established standards for data interchange and querying on the Web. While they have been shown to be useful and applicable in many scenarios, they are not sufficiently adequate for dealing with streams of data and their intrinsic continuous nature. In the last years data and query languages have been proposed to extend both RDF and SPARQL for streams and continuous processing, under the name of RDF Stream Processing – RSP. These efforts resulted in several models and implementations that, at a first look, appear to propose alternative syntaxes but equivalent semantics. However, when asked to continuously answer the same queries on the same data streams, they provide different answers at disparate moments due to the heterogeneity of their operational semantics. These discrepancies render the process of understanding and comparing continuous query results complex and misleading. In this work, the authors propose RSP-QL, a comprehensive model that formally defines the semantics of an RSP system. RSP-QL makes explicit the hidden assumptions of currently available RSP systems, allows defining a formal notion of correctness for RSP query results and, thus, explains why available implementations provide different answers at disparate moments.

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient’s high-risk markers, and support treatment decisions.

Background: Despite the advancement in eXplainable Artificial Intelligence, the explanations provided by model-agnostic predictors still call for improvements (i.e., lack of accurate descriptions of predictors’ behaviors). Contribution: We present a tool for Deep Explanations and Rule Extraction (DEXiRE) to approximate rules for Deep Learning models with any number of hidden layers. Methodology: DEXiRE proposes the binarization of neural networks to induce Boolean functions in the hidden layers, generating as many intermediate rule sets. A rule set is inducted between the first hidden layer and the input layer. Finally, the complete rule set is obtained using inverse substitution on intermediate rule sets and first-layer rules. Statistical tests and satisfiability algorithms reduce the final rule set’s size and complexity (filtering redundant, inconsistent, and non-frequent rules). DEXiRE has been tested in binary and multiclass classifications with six datasets having different structures and models. Results: The performance is consistent (in terms of accuracy, fidelity, and rule length) with respect to the state-of-the-art rule extractors (i.e., ECLAIRE). Moreover, compared with ECLAIRE, DEXiRE has generated shorter rules (i.e., up to 74% fewer terms) and has shortened the execution time (improving up to 197% in the best-case scenario). Conclusions: DEXiRE can be applied for binary and multiclass classification of deep learning predictors with any number of hidden layers. Moreover, DEXiRE can identify the activation pattern per class and use it to reduce the search space for rule extractors (pruning irrelevant/redundant neurons)—shorter rules and execution times with respect to ECLAIRE.

With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning knowledge-based systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state of the art on knowledge engineering in the wind energy domain is performed with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

Quality of life (QoL) assessments are integral to cancer care, yet their effectiveness in providing essential information for supporting survivors varies. This study aimed to elucidate key indicators of QoL among colorectal cancer survivors from the perspective of healthcare professionals, and to evaluate existing QoL questionnaires in relation to these indicators. Two studies were conducted: a Delphi study to identify key QoL indicators and a scoping review of questionnaires suitable for colorectal cancer survivors. Fifty-four healthcare professionals participated in the Delphi study’s first round, with 25 in the second. The study identified two primary QoL domains (physical and psychological) and 17 subdomains deemed most critical. Additionally, a review of 12 questionnaires revealed two instruments assessing the most important general domains. The findings underscored a misalignment between existing assessment tools and healthcare professionals’ clinical priorities in working with colorectal cancer survivors. To enhance support for survivors’ QoL, efforts are needed to develop instruments that better align with the demands of routine QoL assessment in clinical practice.