Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Your Friends Mention It. What About Visiting It? A Mobile Social-Based Sightseeing Application

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing technique

Construindo o Projeto Cuidadosamente: reflexão sobre a saúde mental dos graduandos de Enfermagem frente ao COVID-19

O presente relato de experiência visa destacar a vivência dos autores na construção do Projeto Cuidadosamente em uma universidade privada localizada no município do Rio de Janeiro. Objetiva-se com esse estudo apontar a sua inserção prática, bem como, retratar a importância de um projeto desta magnitude no cuidado à saúde psíquica dos acadêmicos de enfermagem, principalmente no contexto atual de isolamento social pela pandemia de COVID-19. Conclui-se que a ação possibilita a construção de uma rede de apoio entre os próprios colegas de classe que estão experenciando as mesmas dificuldades com esse isolamento social e ameniza situações que possam maximizar ou desencadear algum tipo de transtorno mental, a exemplo de ansiedade e depressão, através de uma escuta qualificada, que é atribuição importante do enfermeiro nos diferentes níveis de assistência. Building the Project Mindfully: reflection on the mental health of nursing students in front of COVID-19The present experience report aims to highlight the authors’ experience in the construction of the Project Mindfully in a private university located in the city of Rio de Janeiro. The objective of this study is to point out its practical insertion, as well as, to portray the importance of a project of this magnitude in the care of the psychic health of nursing students, especially in the current context of social isolation by the pandemic of COVID-19. It is concluded that the action makes it possible to build a support network among the classmates themselves who are experiencing the same difficulties with this social isolation and alleviates situations that can maximize or trigger some type of mental disorder, such as anxiety and depression, through qualified listening, which is an important role of nurses at different levels of care

Visual process maps: a visualization tool for discovering habits in smart homes

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. The visual analysis by domain experts allows to identify stages of human habits that could be automatized or simplified by redesigning the environment. In this paper, we present a visual analysis pipeline for graphically visualizing human habits, starting from the sensor log of a smart space,. We apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed method is employed to automatically extract models to be reused for ambient intelligence. A user evaluation demonstrates the effectiveness of the approach, and compares it with respect to a relevant state-of-the-art visual tool, namely Situvis

Addressing multi-users open challenge in habit mining for a process mining-based approach

Models of human habits in smart spaces can be expressed by using a multitude of formalisms, whose readability influences the possibility of being validated by human experts. Given the growing availability of low-cost sensing devices promoted by the emerging Internet-of-Things, the analysis of huge amount of data produced by these systems will assume an utmost importance in the near future. But most of them are designed for single user cases. Moving forward in their development, often they hardly fit a realistic environment with many users. In this paper, we first review the most relevant approaches in the area during the last decade, and then we present an analysis pipeline that allows, starting from the sensor log of a smart space, to model human habits in a multi-user environment. The approach is based on exploit BLE beacons to discriminate the different users, then applying techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The paper also presents some hints of how the proposed method can be employed to automatically extract models to be reused for ambient intelligence in a multi-users environment

Process-Based Habit Mining: Experiments and Techniques

ndependently of the specific task to be enacted in a smart space, it is always crucial to mine a set of models representing environmental dynamics and, noteworthy, user habits, desires. Many different formalisms have been proposed to model human habits, but the vast majority of them are either difficult to read, evaluate or their definition requires a huge amount of work from either experts or users. In this paper we propose to employ process mining techniques in order to model human habits, we experimentally evaluate such an approach on a dataset built adopting the Smart-Home-in-a-Box toolkit with real users

Micro-accounting for optimizing and saving energy in smart buildings

Energy management, and in particular its optimization, is one of the hot trends in the current days, both at the enterprise level (optimization of whole corporate/government buildings) and singlecitizens’ homes. The current trend is to provide knowledge about the micro(scopic) energy consumption. This allows to save energy, but also to optimize the different energy sources (e.g., solar vs. traditional one) in case of a mixed architecture. In this work, after briefly introducing our specific platform for smart environments able to micro-account energy consumption of devices, we present two case studies of its utilization: energy saving in offices and smart switching among different energy sources