In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified.

Historically, the extraction of coastal line has been performed exploiting optical images, but in the last two decades, some approaches working with synthetic aperture radar (SAR) data have been proposed. Recently, these approaches have been gaining interest due to the availability of high-resolution SAR images. In this letter, a technique for coastal line retrieval from multichannel SAR images is presented. The detection problem is faced in the statistical estimation framework, in particular, exploiting Bayesian estimation theory. The proposed technique is able to detect sea boundaries at full resolution and low error rate in a totally unsupervised way. The performance of the method has been tested using high-resolution COSMO-SkyMed data sets acquired on the Bay of Naples, showing the high accuracy of the proposed technique.

Interferometric synthetic aperture radar (InSAR) systems are capable of providing an estimate of the digital elevation model (DEM) of the imaged ground scene. This is usually done by means of a phase unwrapping (PU) operation. In the absence of additional regularity constraints, PU is an ill-posed problem, because the solution is not unique. Multichannel (MCh) techniques, using stacks of images of the same scene, can be used for restoring the solution uniqueness and reducing the effect of phase noise. Moreover, statistical techniques exploiting the contextual information contained in the data can provide satisfactory results. In this article, an overview of the main MCh statistical DEM reconstruction methods, developed both in the classical and in the Bayesian estimation framework, is presented. In particular, the effectiveness of the exploitation of contextual statistical models is shown by means of numerical experiments on simulated and real data sets.

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer’s disease, meditation might have a potential role in a panel of preventive strategies.

Interferometric synthetic aperture radar (SAR) (InSAR) systems allow 3-D reconstruction of observed scene. In this paper, an innovative approach for phase unwrapping and digital elevation model (DEM) generation using multichannel InSAR data is presented. The proposed algorithm, exploiting both the amplitude and phase of the available complex data, is able to unwrap and simultaneously regularize the observed data. In particular, the exploitation of amplitude data within the unwrapping chain helps in preserving sharp discontinuities typical of urban areas. As a result, the technique provides accurate DEM reconstructions. For this aim, a Markovian approach, together with a new graph-cut-based optimization algorithm, has been considered. The method has been developed specifically to work in urban areas with very high resolution InSAR image stacks, being able to automatically compensate possible phase offsets. Results on both simulated and real case studies are reported, showing the effectiveness of the method.