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Background. Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL. Methods. In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an i-Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition). Results. Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, p = 0.009) although to a limited extent (ES −0.25) restricted to the period after intervention. Conclusions. This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.

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A new deep neural network (NN) approach applied to antenna array adaptive beamforming is presented in this paper. A recurrent NN (RNN) based on the gated recurrent unit (GRU) architecture is used as a beamformer in order to produce proper complex weights for the feeding of the antenna array. The proposed RNN utilizes four hidden GRU layers and one extra layer for linear transformation. The produced weights are subsequently compared with respective weights derived by a null steering beamforming (NSB) technique in order to measure the accuracy of the RNN. The RNN training is performed by using a large data set derived from an NSB technique applied to a realistic microstrip linear antenna array, in order to take into account realworld effects, like the non-isotropic radiation pattern of an array element and the mutual coupling between the array elements. The RNN performance is examined by using the root mean square error metric, whereas its beamforming performance is evaluated by estimating the mean value and the standard deviation of the divergences of the main lobe and nulls directions from their respective desired directions. A comparison between various NN structures and an overall study of the proposed RNN-based beamformer are also presented.

This paper aims to present a novel architecture for the indoors Ambient Assisted Living domain. This domain synthesizes features from the home-based e-health and ambient intelligence scientific areas. The HERA system addresses mainly the needs of elderly in the early stages of the Alzheimer disease aiming to improve the quality of their home life and extend its duration. Other user categories are elderly suffering from cardiovascular diseases and diabetes. The novelty of this architecture is the use of the TV and Set-Top-Box probably already existing in a user's home (and the remote control with which the user is familiar) providing services from an application server integrating agent technology-based personal assistance.