This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the ''Autonomous Cycle of Data Analysis Tasks'' concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.

Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62–99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field.

Serious Games (SGs), defined as a game in which education (in its various forms) is the primary goal rather than entertainment, have been proven as an effective educational tool for engaging and motivating students. However, more research is needed to sustain the suitability of these games to train users with cognitive impairments. This empirical study addresses the use of a SG for training students with Intellectual Disabilities in traveling around the subway as a complement to traditional training. Fifty‐one adult people with Down Syndrome, mild cognitive disability or certain types of Autism Spectrum Disorder, all conditions classified as intellectual disabilities, played the learning game Downtown, a Subway Adventure which was designed ad‐hoc considering their needs and cognitive skills. We used standards‐based Game Learning Analytics techniques (ie, Experience API –xAPI), to collect and analyze learning data both off‐line and in near real‐time while the users were playing the videogame. This article analyzes and assesses the evidence data collected using analytics during the game sessions, like time completing tasks, inactivity times or the number of correct/incorrect stations while traveling. Based on a multiple baseline design, the results validated both the game design and the tasks and activities proposed in Downtown as a supplementary tool to train skills in transportation. Differences between high‐functioning and medium‐functioning users were found and explained in this paper, but the fact that almost all of the students completed at least one route without mistakes, the general improvement trough sessions and the low‐mistake ratio are good indicators about the appropriateness of the game design.

Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis.

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Learning games are becoming popular among teachers as educational tools. However, despite all the game development quality processes (e.g., beta testing), there is no total assurance about the game design appropriateness to the students' cognitive skills until the games are used in the classroom. Furthermore, games designed specifically for Intellectual Disabled (ID) users are even harder to evaluate because of the communication issues that this type of players have. ID users' feedback about their learning experience is complex to obtain and not always fully reliable. To address this problem, we use an evidence-based approach for evaluating the game design of Downtown, A Subway Adventure, a game created to improve independent living in users with ID. In this paper we exemplify the whole process of applying Game Analytics techniques to gather actual users' gameplay interaction data in real settings for evaluating the design. Following this process, researchers were able to validate different game aspects (e.g., mechanics) and could also identify game flaws that may be difficult to detect using formative evaluation or other observational-based methods. Results showed that the proposed evidence-based approach using Game Analytics information is an effective way to evaluate both the game design and the implementation, especially in situations where other types of evaluations that require users' involvement are limited.

In this paper we analyze the process of designing and developing a Serious Game intended to train people with intellectual disabilities in moving around a city using the public transportation system. The first step in our investigation is to understand the cognitive, psychological and motor abilities of our users and their specific needs. Secondly, we translated the characteristics of the players into user requirements, with adapted mechanics to improve the understanding and to increase the probability for the user to be able to carry out the tasks to perform in the video game. Finally, due to the specific characteristics of our final users a Learning Analytics module has been included in the game to collect relevant information about how users are actually playing and to infer how the learning process of every user is occurring. We also discuss the next steps in our research and the future work related with it: design a range of experimental tests to verify the adequacy of the video game as a learning tool for this type of users.

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