Ventricular Dyssynchrony and Pacing-induced Cardiomyopathy in Patients with Pacemakers, the Utility of Ultra-high-frequency ECG and Other Dyssynchrony Assessment Tools

The majority of patients tolerate right ventricular pacing well; however, some patients manifest signs of heart failure after pacemaker implantation and develop pacing-induced cardiomyopathy. This is a consequence of non-physiological ventricular activation bypassing the conduction system. Ventricular dyssynchrony was identified as one of the main factors responsible for pacing-induced cardiomyopathy development. Currently, methods that would allow rapid and reliable ventricular dyssynchrony assessment, ideally during the implant procedure, are lacking. Paced QRS duration is an imperfect marker of dyssynchrony, and methods based on body surface mapping, electrocardiographic imaging or echocardiography are laborious and time-consuming, and can be difficult to use during the implantation procedure. However, the ventricular activation sequence can be readily displayed from the chest leads using an ultra-high-frequency ECG. It can be performed during the implantation procedure to visualise ventricular depolarisation and resultant ventricular dyssynchrony during pacing. This information can assist the electrophysiologist in selecting a pacing location that avoids dyssynchronous ventricular activation

Pioneering steps towards future human-robotic operations performance

Future human space ight exploration missions to the Moon and beyond are hypothesised to benefit from human-robotic integrated operations. The European Space Agency focuses on preparing these operations, following the objective stated in the Global Exploration Roadmap of the International Space Exploration Coordination Group. Currently, human-robotic operations aim at technology development and demonstration, yet essential questions that remain unanswered are: How can human performance be measured, and which metrics are used? This contribution aims at answering this by preparing a pilot phase focusing on human performance assessment for subjects controlling a rover. A recent study by Hosseini (2016) for ESA identified essential knowledge gaps that must be filled to assess astronaut performance for tele-operations, which is of great importance for future missions since they affect mission planning, task allocation and even tool selection. In this regard, this study proposes follow-on research in which a tele-operations experiment is conducted by driving a rover, in order to evaluate human performance in tele-operations. In the previous study, two space-to-ground and multiple ground-to-ground tele-operations experiments were analysed in which subjects controlled a rover. Data analysis studied the command time and execution time of the assigned tasks, i.e. the time it takes for the human to give the command to the rover and the time it takes for the rover to execute its tasks, respectively. Results showed that the main challenge for performance assessment is the lack of recorded parameters. The logged data is limited only to time values and success/failure results and it does not specify performance variations. Furthermore, the study concludes that many diferent sub-tasks were performed in a limited amount of time, resulting in scarce data per sub-task and limiting the statistical significance. Follow-on research is proposed that aims at solving for the two above mentioned issues. Firstly, the study introduces parameters, which are used to assess the performance of pilots regarding neuro-ergonomics and human factors. Secondly, an experiment is set up and is tested for its rigidity in a protocol rehearsal, prior to performing the experiment with a relatively large group of participants. It is hypothesised that this approach has the potential not only to increase the qualitative assessment of the performance, but also to increase the quantitative results essential for preparing crew training and future missions

Real-time data movements acquisition of Taekwondo athletes: first insights

In the last two decades, Taekwondo is a combat sport who has gained popularity, being inducted as an official Olympic Sport since 2000. Lately, more than 4500 federated athletes existing in Portugal have achieved excellent results in national and international competitions recognized by the International Olympic Committee, Portugal Olympic Committee, European Taekwondo Union and World Taekwondo Federation. In this paper, it was performed a study to establish the reliability and accuracy of the Microsoft Kinect 2 in real-time image data collection of the Taekwondo athletes movements. This work will reinforce the research on motion-analysis, targeting the improvement of the athlete’s performance and the establishment of a new level of training and competition, contributing to sport modernization and development.INCT-EN - Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UID/CEC/00319/2013

Guiado háptico mediante aprendizaje por demostración en robótica quirúrgica

[Resumen] Las plataformas robóticas se configuran como una herramienta eficaz a la hora de paliar los inconvenientes que surgen en la aplicación de técnicas quirúrgicas mínimamente invasivas. Este artículo plantea el empleo de guiado háptico para mejorar la experiencia del cirujano cuando utiliza plataformas robóticas teleoperadas. En concreto se propone la técnica de Aprendizaje por Demostración para generar fuerzas que guíen al cirujano durante la ejecución de maniobras específicas. Se ha realizado un estudio preliminar para la validación del método basado en la ejecución de una tarea genérica, la inserción de una clavija en un orificio, que demuestran la viabilidad del método para resolver esta tarea y otras similares relacionadas directamente con la robótica quirúrgica teleoperada.https://doi.org/10.17979/spudc.97884974980

Deep Bilinear Learning for RGB-D Action Recognition

In this paper, we focus on exploring modality-temporal mutual information for RGB-D action recognition. In order to learn time-varying information and multi-modal features jointly, we propose a novel deep bilinear learning framework. In the framework, we propose bilinear blocks that consist of two linear pooling layers for pooling the input cube features from both modality and temporal directions, separately. To capture rich modality-temporal information and facilitate our deep bilinear learning, a new action feature called modality-temporal cube is presented in a tensor structure for characterizing RGB-D actions from a comprehensive perspective. Our method is extensively tested on two public datasets with four different evaluation settings, and the results show that the proposed method outperforms the state-of-the-art approaches.</p