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In this paper, we propose a new indirect method for constructing the radio frequency layer of radio environment map (RF-REM) called self-tuning method (STM). The proposed STM takes into account the characteristics of the operating environment and performs estimation of the transmitter parameters, i.e. its location, antenna diagram, antenna azimuth, transmit power, as well the parameters of the propagation model to obtain the best match between the available measurements and the predicted signal levels. We compare STM to several most often considered existing methods using different numbers of randomly distributed measurements, which could in practice be obtained in a participatory-like manner. The performance evaluation of the methods is performed in terms of averaged root mean square error (RMSE) and 95% confidence interval (CI) calculated between the constructed RF-REMs and the reference RF-REM, as well as in terms of false alarm zone ratio (FAZR) and correct detection zone ratio (CDZR 1). The analysis shows the robustness of the STM to various spatial distributions of measurements and its fast convergence and low residual root mean square error compared to the inverse distance weighted (IDW) method, the inverse distance square weighted method (IDW2), the Kriging method, and the location estimation-based method (LIvE).

Chronic diseases, especially diabetes mellitus, are huge public health burden. Therefore, new health care models for sharing the responsibility for care among health care providers and patients themselves are needed. The concept of empowerment promotes patient's active involvement and control over their own health. It can be achieved through education, self-management, and shared decision making. "ll these aspects can be covered by mobile health technologies, the so-called mHealth. This term comprises mobile phones, patient monitoring devices, tablets, personal digital assistants, other wireless devices, and numerous apps. Many challenges of diabetics can be addressed by mHealth, including glycemic control, nutrition control, physical activity, high blood pressure, medication adherence, obesity, education, diabetic retinopathy screening, diabetic foot screening, and psychosocial care. However, mHealth plays only minor role in diabetes management, despite numerous apps on the market. Namely, these apps have many shortcomings and the majority of them does not include important functions. Moreover, these apps lack the perceived additional benefit by the user and the ease of use, important factors for acceptance of mHealth. Studies of diabetes apps regarding usability and accessibility have shown moderate results. "eside improvements of apps usability, the future of diabetes mHealth lies probably in personalized education and self-management with the help of decision support systems. "t the same time, work on artificial pancreas is in progress and smartphone could be used as user interface.

With the analysis of various sensor data from the mobile devices, it is possible to extract user situations, so-called user context. This is needed for the development of modern, user-friendly services. Therefore, we developed a simple, nonintrusive, and automatic method based on the Wi-Fi fingerprints and GPS. The method finds user stay points, aggregates them into meaningful stay regions, and assigns them four general user contexts: home, work, transit, and free time. We evaluated its performance on the real traces of six different users who annotated their contexts over eight days. The method determined the stay mode of the users with accuracy, precision, and recall of above 96%. In combination with the novel approach for aggregation, all regions relevant to the users were determined. Among the tested aggregation schemes, the fingerprint similarity approach worked the best. The context of the determined stay regions was on average accurately inferred in 98% of the time. For the contexts home, work, and free time, the precision and recall exceeded 86%. The results indicate that the method is robust and can be deployed in various fields where context awareness is desired.

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