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Traditionally, mental health specialists monitor their patients&rsquo, social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson&rsquo, s correlation coefficient &gt, 70%) with individuals&rsquo, social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

Currently, tour guides can be implemented through mobile technologiessuch as smartphones and wearable devices. The penetrationof these technologies into people’s daily lives has made it possibleto implement more sophisticated and personalized services, revolutionizingthe tourism industry. However, the process of developingsuch applications is complex and involves the knowledge of variousexperts, such as programmers and designers. So this article devisedan authoring tool titled inTourMobile, which allows non-expertpeople to develop their mobile tour guides easily and intuitively.The usability of the designed tool was evaluated, in which it wasobserved its efficiency to assist in the development of mobile tourguides.

Traditionally, the process of monitoring and evaluating social behavior related to mental health has based on self-reported information, which is limited by the subjective character of responses and by various cognitive biases. Today, however, computational methods can use ubiquitous devices to monitor social behaviors related to mental health rather than relying on self-reports. Therefore, these technologies can be used to identify the routine of social activities, which enables the recognition of abnormal behaviors that may be indicative of mental disorders. In this paper, we present a solution for detecting context-enriched sociability patterns. Specifically, we introduced an algorithm capable of recognizing the social routine of monitored people. To implement the proposed algorithm, it was used a set of Complex Event Processing (CEP) rules, which allow the continuous processing of the social data stream derived from ubiquitous devices. The experiments performed indicated that the proposed solution is capable of detecting sociability patterns similar to a batch algorithm and demonstrated that context-based recognition provides a better understanding of social routine.

Diante do atual contexto educacional, percebese dificuldade em motivar e envolver os estudantes nas atividades escolares por meio de métodos tradicionais de ensino. Pesquisadores acreditam que esta insatisfação pode ser causada pelo fato de que os jovens que convivem com mídias digitais em seu dia a dia tenham dificuldades para aprender por meio dos métodos tradicionais. Assim, este artigo desenvolveu um Jogo Móvel Baseado em Localização (JMBL) com recursos de Realidade Aumentada (RA). O objetivo é proporcionar aos estudantes um ambiente lúdico e motivador para atividades escolares. Por meio de uma avaliação ergonômicopedagógica, os resultados demonstraram que o JMBL projetado permite uma experiência agradável ao usuário e possui uma estrutura pedagógica capaz de contribuir significativamente para a aprendizagem do estudante.

Traditionally, the process of monitoring and evaluating social behavior related to mental health has based on self-reported information, which is limited by the subjective character of responses and various cognitive biases. Today, however, there is a growing amount of studies that have provided methods to objectively monitor social behavior through ubiquitous devices and have used this information to support mental health services. In this paper, we present a Systematic Literature Review (SLR) to identify, analyze and characterize the state of the art about the use of ubiquitous devices to monitor users' social behavior focused on mental health. For this purpose, we performed an exhaustive literature search on the six main digital libraries. A screening process was conducted on 160 peer-reviewed publications by applying suitable selection criteria to define the appropriate studies to the scope of this SLR. Next, 20 selected studies were forwarded to the data extraction phase. From an analysis of the selected studies, we recognized the types of social situations identified, the process of transforming contextual data into social situations, the use of social situation awareness to support mental health monitoring, and the methods used to evaluate proposed solutions. Additionally, we identified the main trends presented by this research area, as well as open questions and perspectives for future research. Results of this SLR showed that social situation-aware ubiquitous systems represent promising assistance tools for patients and mental health professionals. However, studies still present limitations in methodological rigor and restrictions in experiments, and solutions proposed by them have limitations to be overcome.