A sensing architecture for empathetic data systems

Today's increasingly large and complex databases require novel and machine aided ways of exploring data. To optimize the selection and presentation of data, we suggest an unconventional approach. Instead of exclusively relying on explicit user input to specify relevant information or to navigate through a data space, we exploit the power and potential of the users' unconscious processes in addition. To this end, the user is immersed in a mixed reality environment while his bodily reactions are captured using unobtrusive wearable devices. The users' reactions are analyzed in real-time and mapped onto higher-level psychological states, such as surprise or boredom, in order to trigger appropriate system responses that direct the users' attention to areas of potential interest in the visualizations. The realization of such a close experience-based human-machine loop raises a number of technical challenges, such as the real-time interpretation of psychological user states. The paper at hand describes a sensing architecture for empathetic data systems that has been developed as part of such a loop and how it tackles the diverse challenges

Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORStroke is one of the main causes of disabilities caused by injuries to the human central nervous system, yielding a wide range of mild to severe impairments that can compromise sensorimotor and cognitive functions. Although rehabilitation protocols may improve function of stroke survivors, patients often reach plateaus while undergoing therapy. Recently, virtual reality (VR) technologies have been paired with traditional rehabilitation aiming to improve function recovery after stroke. Aiming to better understand structural brain changes due to VR rehabilitation protocols, we modeled the brain as a graph and extracted three measures representing the network's topology: degree, clustering coefficient and betweenness centrality (BC). In this single case study, our results indicate that all metrics increased on the ipsilesional hemisphere, while remaining about the same at the contralesional site. Particularly, the number of functional connections increased in the lesion area overtime. In addition, the BC displayed the highest variations, and in brain regions related to the patient's cognitive and motor impairments; hence, we argue that this measure could be regarded as an indicative for brain plasticity mechanisms.891894FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIOR2013/07559-3Sem informação9. IEEE/EMBS International Conference on Neural Engineering (NER)20 a 23 de Março de 2019San Francisco, CA, Estados UnidosIEEE; EMB

Towards a synthetic tutor assistant: The EASEL project and its architecture

Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learner’s progress, discrimination of the learner’s utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASEL’s unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions

FocusDET: Herramienta multimodal para la localización del foco epileptógeno en la epilepsia farmacorresistente

Los pacientes epilépticos con crisis parciales complejas resistentes a tratamiento farmacológico son candidatos a la escisión de la región focal del cerebro que induce dichas crisis. La correcta localización del foco epileptógeno es esencial para considerar la cirugía como posible tratamiento. El objetivo de este trabajo es el desarrollo de una aplicación médica para la localización del foco epileptógeno a partir de datos multimodales. Para el desarrollo de esta nueva herramienta se utiliza GIMIAS, una plataforma de software para la implementación y prototipado de aplicaciones médicas. La nueva herramienta desarrollada, FocusDET, permite llevar a cabo la técnica SISCOM y el análisis de datos EEG-RM f ictal, de imágenes PET y de distintas modalidades de imagen de RM. FocusDET, gracias a su interfaz amigable y a su rapidez de procesamiento, puede ser adecuada para la rutina clínica

Prototype Tool for the Extraction of the Coronary Vessels Centreline.

International audienc

Advanced interfaces to stem the data deluge in mixed reality: Placing human (un)consciousness in the loop

We live in an era of data deluge and this requires novel tools to effectively extract, analyze and understand the massive amounts of data produced by the study of natural and artificial phenomena in many areas of research

Integrative Software Framework for a Better Understanding, Patient Selection and Planning of Cardiac Resynchronization Therapy Patients.

International audienc