Human Activity Recognition for Indoor Localization Using Smartphone Inertial Sensors.

With the fast increase in the demand for location-based services and the proliferation of smartphones, the topic of indoor localization is attracting great interest. In indoor environments, users' performed activities carry useful semantic information. These activities can then be used by indoor localization systems to confirm users' current relative locations in a building. In this paper, we propose a deep-learning model based on a Convolutional Long Short-Term Memory (ConvLSTM) network to classify human activities within the indoor localization scenario using smartphone inertial sensor data. Results show that the proposed human activity recognition (HAR) model accurately identifies nine types of activities: not moving, walking, running, going up in an elevator, going down in an elevator, walking upstairs, walking downstairs, or going up and down a ramp. Moreover, predicted human activities were integrated within an existing indoor positioning system and evaluated in a multi-story building across several testing routes, with an average positioning error of 2.4 m. The results show that the inclusion of human activity information can reduce the overall localization error of the system and actively contribute to the better identification of floor transitions within a building. The conducted experiments demonstrated promising results and verified the effectiveness of using human activity-related information for indoor localization.

An Infrastructure-Free Magnetic-Based Indoor Positioning System with Deep Learning.

Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user's location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.

Fingerprints and Floor Plans Construction for Indoor Localisation Based on Crowdsourcing.

The demand for easily deployable indoor localisation solutions has been growing. Although several systems have been proposed, their limitations regarding the high implementation costs hinder most of them to be widely used. Fingerprinting-based IPS (Indoor Positioning Systems) depend on characteristics pervasively available in buildings. However, such systems require indoor floor plans, which might not be available, as well as environmental fingerprints, that need to be collected through human resources intensive processes. To overcome these limitations, this paper proposes an algorithm for the automatic construction of indoor maps and fingerprints, solely depending on non-annotated crowdsourced data from smartphones. Our system relies on multiple gait-model based filtering techniques for accurate movement quantification in combination with opportunistic sensing observations. After the reconstruction of users' movement with PDR (Pedestrian Dead Reckoning) techniques, Wi-Fi measurements are clustered to partition the trajectories into segments. Similar segments, which belong to the same cluster, are identified using an adaptive approach based on a geomagnetic field distance. Finally, the floor plans are obtained through a data fusion process. Merging the acquired environmental data using the obtained floor plan, fingerprints are aligned to physical locations. Experimental results show that the proposed solution achieved comparable floor plans and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free IPS.

Transferencia, contratransferencia e os equivocos

O presente estudo resume alguns conceitos sobre transferencia e contratransferencia. Estes conceitos sao considerados de fundamental importancia para a terapia ou analise. A maneira de abordagem em que o terapeuta tratara estes assuntos direcionara sua acao sobre o paciente. Basta dizer que os conceitos citados sao equivocos e nao podem ser univocos desde que diferentes autores os considera a partir de diferentes conceitos. Embora algumas caracteristicas sejam comuns, nossa intencao aqui e somente elucidar conceitos existentes.

Os mandatos sociais e a construção da subjetividade: delito, violência e marginalidade

O presente estudo trata a questão da construção subjetiva via 'mandatos sociais'. Os sujeitos pesquisados foram 10 presos do sexo masculino recolhidos a uma instituição carcerária. Eles mesmos efeitos do sintoma desta construção subjetiva. O tema é abordado em uma perspectiva social e psicológica a partir de entrevistas e sob o enfoque metodológico da fenomenologia. A investigação centralizou-se na verbalização e busca de sentido para os sujeitos e de sua condição da marginalizados, havendo posteriormente a transformação destas verbalizações para uma linguagem analítica. Conclui-se que para fins da construção da subjetividade é necessário um 'mandato social' que estruturado num sintoma produz sujeitos e seus efeitos: delito, violência e marginzalização (AU)