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This paper addresses the problem of converting a conventional video stream based on sequences of frames into the spike event-based representation known as the address-event-representation (AER). In this paper we concentrate on rate-coded AER. The problem is addressed as an algorithmic problem, in which different methods are proposed, implemented and tested through software algorithms. The proposed algorithms are comparatively evaluated according to different criteria. Emphasis is put on the potential of such algorithms for a) doing the frame-based to event-based representation in real time, and b) that the resulting event streams ressemble as much as possible those generated naturally by rate-coded address-event VLSI chips, such as silicon AER retinae. It is found that simple and straightforward algorithms tend to have high potential for real time but produce event distributions that differ considerably from those obtained in AER VLSI chips. On the other hand, sophisticated algorithms that yield better event distributions are not efficient for real time operations. The methods based on linear-feedback-shift-register (LFSR) pseudorandom number generation is a good compromise, which is feasible for real time and yield reasonably well distributed events in time. Our software experiments, on a 1.6-GHz Pentium IV, show that at 50% AER bus load the proposed algorithms require between 0.011 and 1.14 ms per 8 bit-pixel per frame. One of the proposed LFSR methods is implemented in real time hardware using a prototyping board that includes a VirtexE 300 FPGA. The demonstration hardware is capable of transforming frames of 64 times; 64 pixels of 8-bit depth at a frame rate of 25 frames per second, producing spike events at a peak rate of 107 events per second. ; European Union IST-2001-34124 ; Gobierno de España TIC-2000-0406-P4, TIC-2003-08164-C03-01

Background: Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population. Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, "Ensure healthy lives and promote well-being for all at all ages"), and to suggest possible reasons for these gaps as well as to propose some solutions. Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary taxonomy. Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and behavior change theories were not observed in any of the selected studies, did not use principles of tailoring, and did not assess (cost)-effectiveness. Discussion: Health recommender systems may be further improved by using relevant behavior change strategies and by implementing essential characteristics of tailored interventions. In addition, many of the features required to assess each of the domain aspects, the methodological and procedural aspects, and technical aspects were not reported in the studies. Conclusions: The studies analyzed presented few evidence in support of the positive effects of using health recommender systems in terms of cost-effectiveness and patient health outcomes. This is why future studies should ensure that all the proposed features are covered in our multidisciplinary taxonomy, including integration with electronic health records and the incorporation of health promotion theoretical factors and behavior change theories. This will render those studies more useful for policymakers since they will cover all aspects needed to determine their impact toward meeting SDG3. ; European Union's Horizon 2020 No 681120

Background Mobile apps have a great potential to support patients in healthcare, and to encourage healthy behavioral changes such as smoking cessation. Nevertheless, the user rejection levels are still high. A set of factors that has impact on the app effectiveness is related to the quality of those features that lead to positive user experiences when using the app. This work aims to evaluate the user experience, and more specifically the usability and the user satisfaction with a mobile application for smoking cessation. This will also provide a basis for future improvements. Methods We provided a smoking cessation mobile Android app to two different user cohorts, the smokers as valid users and the experts, for three weeks. The app featured usual functionalities to help quit smoking, including an achieved benefits section, mini-games to distract during cravings, and supportive motivational messages. We collected information about user experience, through game playability and message satisfaction questionnaires, and the experts' opinions. We also considered usage of app sections, the duration of the mini-game sessions, and the user ratings for motivational messages. Results We included 45 valid users and 25 experts in this study. The questionnaire indicated 80% satisfaction rate for the motivational messages. According to game questionnaires, over 69% of the participants agreed that the games have good usability features, however, for questions related to mobility and gameplay heuristics, agreements were below 67%. The most accessed app sections were achieved benefits and the one with motivational messages. The experts described issues that could help to improve the application. Conclusions The combination of questionnaires with expert reports allowed to identify several problems and possible corrections. Our study showed that motivational messages have a good satisfaction rate, although it is necessary to consider technical features of some mobile devices that may hinder message reception. Games have good usability and it's expected that the addition of difficulty levels and a better accessibility to the game menu could make them more attractive and increase its usage. Future development of mHealth apps based on gamification and motivational messages need to consider these factors for better user satisfaction and usability. ; European Union's Horizon 2020 No 681120

et al. ; This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies. © 2009 IEEE. ; This work was supported by the European Commission under Grant IST-2001-34124 (CAVIAR). The work of R. Serrano-Gotarredona was supported by the Spanish Ministry of Education and Science under FPU scholarship. The work of L. Camuñas-Mesa was supported by the Spanish Ministry of Education and Science under FPI scholarship. ; Peer Reviewed

El glaucoma es una de las principales causas de ceguera. Cambia la morfología del nervio óptico. La copa es la región central del disco óptico. El CDR (relación del diámetro de la copa y el disco) es un indicador del estado de la enfermedad. Un CDR normal se encuentra entre 0.3 y 0.4. A mayor CDR Glaucoma más avanzado. En este trabajo se pretende demostrar la viabilidad de los algoritmos en los que la forma del disco y la copa son aproximadamente círculos. En estos casos el número de parámetros es mucho menor y, al menos, en nuestras pruebas iniciales la aproximación es más realista que con las formas originales para su implementación en aprendizaje profundo. ; Glaucoma is one of the main causes of blindness. It changes the morphology of the optic nerve. The cup is the central region of the optical disc. The CDR (ratio of the diameter of the cup and disc) is an indicator of the state of the disease. A normal CDR is between 0.3 and 0.4. A higher CDR more advanced glaucoma. In this work we try to demonstrate the viability of algorithms in which the shape of the disc and the cup are approximately circles. In these cases, the number of parameters is much lower and, at least, in our initial tests the approach is sometimes more realistic than with the original forms for a Deep learning implementation. ; NPP project funded by SAIT (2015-2018) ; Spanish government grant European Regional Development Fund COFNET TEC2016-77785-P

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies. ; European Commission IST-2001-34124