ObjectiveModern technologies are increasingly used in the development of cognitive interventions for older adults. Research into possible applications of virtual reality in such interventions has begun only recently. The aim of present study was to evaluate the effects of 8 sessions of VR-based cognitive training using the GRADYS game in healthy older adults (n = 72; aged 60–88) and older adults living with mild dementia (n = 27; aged 60–89).ResultsOlder adults with mild dementia demonstrated worse baseline cognitive performance than participants without dementia. Both groups showed progress in training, which was greater in healthy older adults. There were also significant differences in cognitive functioning before and after the training. However, positive changes were revealed almost exclusively in the group of older adults without dementia. Based on the findings, we can recommend the GRADYS game for cognitive enhancement and as a possible counter-measure for cognitive decline experienced in normal cognitive ageing. Our results provide also support for the usefulness of VR technology in cognitive interventions in older adults. The use of the GRADYS game in persons living with dementia, however, would require several of the hardware and software modifications.Trial registration ISRCTN17613444, date of registration: 10.09.2019. Retrospectively registered

The article aims to identify the latest trends in research on renewable energy, sustainability and the environment. A total of 92,873 publications from 123 Scopus sources for 2020–2021 are compared using the scoping review method. The results show that the most cited works in this sample are those by authors from the Asian region. The research of these authors focuses on the security, efficiency and reliability of separate elements in energy systems. Besides, the paper considers the problems regarding COVID disease along with the renewable energy sources, perovskite and organic solar panels, nanostructured materials and high energy density. Finally, the paper analyses applications of computer science methods in research on renewable energy, sustainability and the environment. The findings evidently show that recent advancements in computer science methods were not extensively used in the discussed research domain and give a great room for novel strategies of prognosing, simulation and processes optimisation.

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

One of the key technologies that lays behind the human–machine interaction and human motion diagnosis is the limbs motion tracking. To make the limbs tracking efficient, it must be able to estimate a precise and unambiguous position of each tracked human joint and resulting body part pose. In recent years, body pose estimation became very popular and broadly available for home users because of easy access to cheap tracking devices. Their robustness can be improved by different tracking modes data fusion. The paper defines the novel approach—orientation based data fusion—instead of dominating in literature position based approach, for two classes of tracking devices: depth sensors (i.e., Microsoft Kinect) and inertial measurement units (IMU). The detailed analysis of their working characteristics allowed to elaborate a new method that let fuse more precisely limbs orientation data from both devices and compensates their imprecisions. The paper presents the series of performed experiments that verified the method’s accuracy. This novel approach allowed to outperform the precision of position-based joints tracking, the methods dominating in the literature, of up to 18%.

Abstract. Eye-gaze tracking is an aspect of human-computer interaction still growing in popularity,. Tracking human gaze point can help control user interfaces and may help evaluate graphical user interfaces. At the same time professional eye-trackers are very expensive and thus unavailable for most of user interface researchers and small companies. The paper presents very effective, low cost, computer vision based, interactive eye-gaze tracking method. On contrary to other authors results the method achieves very high precision (about 1.5 deg horizontally and 2.5 deg vertically) at 20 fps performance, exploiting a simple HD web camera with reasonable environment restrictions. The paper describes the algorithms used in the eye-gaze tracking method and results of experimental tests, both static absolute point of interest estimation, and dynamic functional gaze controlled cursor steering.