Cloud-based platforms have gained popularity over the years because they can be used for multiple purposes, from synchronizing contact information to storing and managing user fitness data. These platforms are still in constant development and, so far, most of the data they store is entered manually by users. However, more and better wearable devices are being developed that can synchronize with these platforms to feed the information automatically. Another aspect that highlights the link between wearable devices and cloud-based health platforms is the improvement in which the symptomatology and/or physical status information of users can be stored and syn-chronized in real-time, 24 h a day, in health platforms, which in turn enables the possibility of synchronizing these platforms with specialized medical software to promptly detect important variations in user symptoms. This is opening opportunities to use these platforms as support for monitoring disease symptoms and, in general, for monitoring the health of users. In this work, the characteristics and possibilities of use of four popular platforms currently available in the market are explored, which are Apple Health, Google Fit, Samsung Health, and Fitbit.

In this paper a new boundary constraint-handling technique called “centroid” is proposed to keep the search within the valid ranges of decision variables in a constrained numerical optimization problem. Such technique is based on computing the centroid of three solutions within the search space, one taken from the population and two generated at random. A comparison of the proposed technique in three experiments against other approaches found in the specialized literature is carried out by using a well-known scalable benchmark of 18 test problems. The results show that the proposed technique is able to promote better final results and improving both, the approach to the feasible region and the ability to generate better solutions.

In this paper, an empirical comparison of four micro differential evolution variants to solve partial instances of the Full Model Selection (FMS) problem on time-series databases is presented. Two smoothing methods, three time-series representations, and one classifier, all of them with their corresponding parameters, are optimized by the evolutionary search. The study focuses on the effects of the crossover operator and the type of base vector used in the differential mutation. The four variants are tested in three experiments to analyze the final statistical results, the convergence behavior and the type of optimized model obtained. Six temporal databases with different features are solved. The results, which are statistically validated by using non-parametric tests, suggest that the four variants are able to provide competitive results. However, using the best vector in the current population, coupled with a binomial crossover, allow the micro differential evolution to reach better final results, while the opposite (base vector chosen at random and exponential crossover) help to find a good model with less solution evaluations in temporal databases whose models are very expensive to evaluate.