27,004 research outputs found

    ssMousetrack: Analysing computerized tracking data via Bayesian state-space models in {R}

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    Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By adopting instantaneous tracking devices such as computer-mouse, wii, and joysticks, such data provide new insights for analysing the dynamic unfolding of response process. ssMousetrack is a R package for modeling and analysing computerized-tracking data by means of a Bayesian state-space approach. The package provides a set of functions to prepare data, fit the model, and assess results via simple diagnostic checks. This paper describes the package and illustrates how it can be used to model and analyse computerized-tracking data. A case study is also included to show the use of the package in empirical case studies

    Collaborative Gaze Channelling for Improved Cooperation During Robotic Assisted Surgery

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    The use of multiple robots for performing complex tasks is becoming a common practice for many robot applications. When different operators are involved, effective cooperation with anticipated manoeuvres is important for seamless, synergistic control of all the end-effectors. In this paper, the concept of Collaborative Gaze Channelling (CGC) is presented for improved control of surgical robots for a shared task. Through eye tracking, the fixations of each operator are monitored and presented in a shared surgical workspace. CGC permits remote or physically separated collaborators to share their intention by visualising the eye gaze of their counterparts, and thus recovers, to a certain extent, the information of mutual intent that we rely upon in a vis-à-vis working setting. In this study, the efficiency of surgical manipulation with and without CGC for controlling a pair of bimanual surgical robots is evaluated by analysing the level of coordination of two independent operators. Fitts' law is used to compare the quality of movement with or without CGC. A total of 40 subjects have been recruited for this study and the results show that the proposed CGC framework exhibits significant improvement (p<0.05) on all the motion indices used for quality assessment. This study demonstrates that visual guidance is an implicit yet effective way of communication during collaborative tasks for robotic surgery. Detailed experimental validation results demonstrate the potential clinical value of the proposed CGC framework. © 2012 Biomedical Engineering Society.link_to_subscribed_fulltex

    Anchorage of Innovations: Assessing Dutch efforts to use the greenhouse effect as an energy source

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    This case study concerns the Dutch glasshouse horticulture sector which is responsible for 10% of the country’s natural gas consumption. Various developments resulted in internal as well as external pressures to bring this down. This has led to a variety of ‘alternative energy approaches’ for the sector. The interactions between niche and regime, however, are not well understood. Building on Loeber (2003) the concept of ‘anchorage’ to analyse this interaction is used. It is concluded that the concept of anchorage provides a useful tool to study the interaction between niche and regime and the crooked pathways of ‘innovation in the making

    Production of High-Intensity, Highly Charged Ions

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    In the past three decades, the development of nuclear physics facilities for fundamental and applied science purposes has required an increasing current of multicharged ion beams. Multiple ionization implies the formation of dense and energetic plasmas, which, in turn, requires specific plasma trapping configurations. Two types of ion source have been able to produce very high charge states in a reliable and reproducible way: electron beam ion sources (EBIS) and electron cyclotron resonance ion sources (ECRIS). Multiple ionization is also obtained in laser-generated plasmas (laser ion sources (LIS)), where the high-energy electrons and the extremely high electron density allow step-by-step ionization, but the reproducibility is poor. This chapter discusses the atomic physics background at the basis of the production of highly charged ions and describes the scientific and technological features of the most advanced ion sources. Particular attention is paid to ECRIS and the latest developments, since they now represent the most effective and reliable machines for modern accelerators.Comment: 42 pages, contribution to the CAS-CERN Accelerator School: Ion Sources, Senec, Slovakia, 29 May - 8 June 2012, edited by R. Baile

    An original framework for understanding human actions and body language by using deep neural networks

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    The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

    Towards a human-in-the-loop control for a smart orthotic system

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    Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Stroke is the main cause of paralysis. This pathology has provoked a considerable increase of persons with motor impairments. With a therapy focused on each clinical case, the total or partial recovery can be achieved. Powered orthoses have been developed to promote an effective recover, based on repetitive gait training and user’s active participation. Many control approaches have been developed to control these devices, but none of them promotes an user-oriented strategy focused to the user’s needs. In an attempt of solving this issue, a new approach named Human-in-the-loop is emerging. This strategy allows the adaptation of some assistive parameters based on the user’s energetic cost, promoting a therapy tailored to each end-user needs. However, to estimate the energy expenditure, the use of non-ergonomic sensors, not suitable for clinical context, is required. Thus, it is necessary to find new ways of estimating energy expenditure using wearable and comfortable sensors. In this dissertation, the first steps to introduce the Human-in-the-loop strategy into a powered orthosis are presented. For this purpose, two strategies were developed: a strategy that allows the angular trajectory adaptation in real-time and other that promotes a stiffness adaptation all over the gait cycle. Both strategies were validated with healthy subjects. In the first strategy, the orthosis was able to modify its assistance in a fraction of microseconds, and the end-users were able to follow her with a median error below 10%. Regarding the second strategy, the results show that the orthosis allowed an effective change in the systems’ interaction stiffness, promoting an active participation of each user during its assistance. The energetic impact of using the robotic assistive device is also presented. As it promotes an energy expenditure augmentation in more than 30% in comparison to walk without the device, the necessity of implementing the Human-in-the-loop strategy was highlighted. In an attempt of finding an ergonomic technique to estimate the energetic cost, the use of machine learning algorithms was tested. The results, obtained with a MLP and a LSTM, prove that it is possible to estimate the energy expenditure with a mean error close to 11%. Future work consists in the implementation of the model in real-time and the collection of more data with the aforementioned control approaches, in a way of constructing a more robust model.O AVC é uma das maiores causas de paralisia. Esta patologia, cada vez mais com maior incidência nos jovens, tem provocado um aumento considerável de pessoas com problemas de mobilidade. Com uma terapia focada a cada caso clínico, a recuperação total ou parcial pode ser conseguida. As ortóteses ativas têm vindo a ser desenvolvidas com o propósito de promover uma recuperação eficaz, baseada em treinos repetitivos e numa participação ativa dos utilizadores. Várias abordagens de controlo têm vindo a ser desenvolvidas para controlar estes dispositivos, mas nenhuma delas promove uma estratégia orientada às necessidades do utilizador. Na tentativa de solucionar este problema, uma nova abordagem, designada por Human-in-the-loop está a emergir. Baseada no custo energético, esta estratégia permite adaptar parâmetros da assistência, promovendo uma terapia focada e direcionada a cada utilizador. No entanto, para estimar o custo energético, recorre-se ao uso de sensores que não são adequados para contexto clínico. Assim, torna-se necessário estudar novas formas de estimar o custo energético. Nesta dissertação são apresentados os primeiros passos para introduzir o controlo Human-in-the-loop numa ortótese ativa. Para isso, duas estratégias foram apresentadas: uma estratégia que permite adaptar a trajetória angular da ortótese, em tempo real, e outra que promove a adaptação da complacência do sistema ao longo do ciclo da marcha. Ambas foram validadas com sujeitos saudáveis. Relativamente à primeira abordagem, a ortótese foi capaz de modificar a sua assistência em microssegundos, e os utilizadores foram capazes de a seguir com um erro mediano inferior a 10%. No que diz respeito à segunda abordagem, os resultados mostram que a ortótese promoveu uma alteração eficaz da complacência de interação, promovendo uma participação ativa do utilizador durante a sua assistência. O impacto energético do uso do sistema robótico é, também, apresentado. Promovendo um aumento do custo energético em mais de 30%, a necessidade da estratégia Human-in-the-loop foi realçada. Na tentativa de encontrar uma técnica para estimar o custo energético, recorreu-se ao uso de machine learning. Os resultados, obtidos com uma MLP e uma LSTM, provam que é possível estimar o custo energético com um erro médio próximo dos 11%. Trabalho futuro passa pela implementação do modelo em tempo real e a recolha de mais dados com as abordagens de controlo apresentadas, de forma a construir um modelo mais robusto
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