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Data comprise intracranial EEG (iEEG) brain activity represented by stereo EEG (sEEG) signals, recorded from over 100 electrode channels implanted in any one patient across various brain regions. The iEEG signals were recorded in epilepsy patients (N = 10) undergoing invasive monitoring and localization of seizures when they were performing a battery of four memory tasks lasting approx. 1 hour in total. Gaze tracking on the task computer screen with estimating the pupil size was also recorded together with behavioral performance. Each dataset comes from one patient with anatomical localization of each electrode contact. Metadata contains labels for the recording channels with behavioral events marked from all tasks, including timing of correct and incorrect vocalization of the remembered stimuli. The iEEG and the pupillometric signals are saved in BIDS data structure to facilitate efficient data sharing and analysis., Measurement(s)intracranial electroencephalogram measurement • gaze • pupil size traitTechnology Type(s)intracranial electroencephalography (iEEG) • gaze tracking • PupillometryFactor Type(s)patient • taskSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16847566

Virtual reality (VR) has been on the scene for several decades already. Its first applications were in gaming. However, hardware and software were expensive and thus not for everybody. Since that time, the development of technology proceeded fast and enabled to open new application areas for VR. Currently many commercial systems are available for gaming, training and education, simulations, design, and also for medical purposes. In the article we focus on VR applications in healthcare. First we present existing commercial solutions, and research studies showing the potential of VR in healthcare. In recent years there have appeared many interesting projects and applications aimed at ageing population as target users. We present examples of such projects. Based on our previous experience and after analysis of available solutions, we propose a conceptual architecture od software environment for development of such applications and discuss their potential use. Finally, the implementation of the proposed architecture for interactive application of experience sets is described.

Electrical stimulation is a powerful therapeutic tool for treating neurologic and neuropsychiatric disorders. Sensing and modulating electrophysiological biomarkers of memory over extended timescales is necessary for tracking and improving memory in humans. Here, we describe results from humans in their natural home environments using a novel, investigational system enabling chronic stimulation and multi-channel recording of anterior thalamic and hippocampal local field potentials during memory tasks. Four people with focal epilepsy performed a free recall verbal memory task repeatedly for up to fifty months on a touch-screen device with wireless signal acquisition with electrophysiology and behavioral data streaming to a cloud environment. Anterior thalamic-hippocampal spectral activities in the theta frequency range were found to correlate with memory processing, to predict task performance, and to be modulated by deep brain stimulation. Our results provide a new biomarker-based technology for chronic remote tracking of memory performance and modulation of the associated neural activities.One Sentence SummaryElectrical stimulation in the anterior thalamic nuclei modulates theta frequency activities and improves human verbal memory performance chronically.

In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detection of anatomical landmarks under different conditions. We infer the quality of detection based on the keypoint confidence values reported by the OpenPose. We used more than two thousand unique exercises for the evaluation. We focus on the influence of the camera view and the influence of the position of the trainees, which are essential in terms of the use for home exercise. Our results show that the position of the trainee has the greatest effect, in the following increasing order of suitability across all camera views: lying position, position on the knees, sitting position, and standing position. On the other hand, the effect of the camera view was only marginal, showing that the side view is having slightly worse results. The results might also indicate that the quality of detection of lower body joints is lower across all conditions than the quality of detection of upper body joints. In this practical overview, we present the possibilities and limitations of current camera-based systems in telerehabilitation.

SGS20/214/OHK3/3T /37 ”Active learning of time series models”, the project No.FV −20696 “Personal health monitoring and assistive systems” The aim of this work is to assess the minimal technical requirements for using a simple RGB camera for motion sensing in a standard home environment. We experimentally verify the recording requirements for subsequent motion analysis. Our work contributes to the development of physical telerehabilitation without the need to use special HW and thus enable telerehabilitation for the general public, which is especially important during the COVID-19 lockdown. We have found out that such a system can work surprisingly well even with a low-cost camera in poor recording conditions and slow 3G internet connection.

There are not many studies dealing with a comparison of the eye movements of individuals with dyslexia and developmental language disorder (DLD). The aim of this study is to compare the eye movements in the two most common language disorders, dyslexia and DLD and to consider their contribution to diagnostics. In the research the oculomotor test was administered to 60 children with the clinical diagnosis of dyslexia or DLD and 58 typically developing children (controls). The test included a prosaccadic task, antisaccadic task and a nonverbal sequential task with self-regulation of the pace. Controls could be singled out from other two clinical groups by means of the oculomotor imaging. Both of the clinical groups in comparison with the controls were characterized by worse overall performance. Through the employment of the oculomotor it was possible to differentiate between both of the clinical groups. The dyslexics had an overall worse oculomotor performance than the DLD group. The results of the study show that the oculomotor test has the potential to contribute to diagnostics of dyslexia and DLD and the screening of these disorders at pre-school age.

The posture of body segments can be negatively influenced by many diseases of the nervous, visual and musculoskeletal systems. This article outlines a newly designed system and related procedures to record and evaluate anatomical body angles. The system is equipped with two mutually calibrated cameras allowing the evaluation of body movement in two anatomical planes. The hardware part of the camera system and calibration method was designed for practical use in clinical practice. Moreover, the proposed camera system allows for the recording and evaluation of motion data in a home environment or at a safe distance. It enables non-invasive and non-contact measuring of body segments and, therefore, can be used in distance rehabilitation and distance diagnosing. The study also demonstrates the hardware’s performance accelerator based on a human body tracking algorithm. The device utilizes the third party algorithms, such as OpenPose, for the extraction of major body points from selected video frames. Any missing data points are then interpolated through the proposed tracking algorithm. This procedure results in an acceleration of the overall hardware performance.

In this paper, we follow up on research dealing with body tracking and motor rehabilitation. We describe the current situation in telerehabilitation in the home environment. Existing solutions do not allow wide adoption due to hardware requirements and complicated setup. We come with the possibility of telerehabilitation using only laptop or mobile web camera. Together with physiotherapists, we have compiled a set of complex motor exercises to show that the system can be practically used.

Pupil responses are known to indicate brain processes involved in perception, attention and decision-making. They can provide an accessible biomarker of human memory performance and cognitive states in general. Here we investigated changes in the pupil size during encoding and recall of word lists. Consistent patterns in the pupil response were found across and within distinct phases of the free recall task. The pupil was most constricted in the initial fixation phase and was gradually more dilated through the subsequent encoding, distractor and recall phases of the task, as the word items were maintained in memory. Within the final recall phase, retrieving memory for individual words was associated with pupil dilation in absence of visual stimulation. Words that were successfully recalled showed significant differences in pupil response during their encoding compared to those that were forgotten – the pupil was more constricted before and more dilated after the onset of word presentation. Our results suggest pupil size as a potential biomarker for probing and modulation of memory processing.

First virtual reality (VR) applications were in gaming. Since that time, both the hardware and software reached maturity and new areas for applications opened. Industry and healthcare are very different application areas. But they also share some similarities, in particular simulations as close to reality as possible allow to train necessary skills without the danger of damaging machinery or causing a patient injury and to avoid critical situations. We present several examples of VR applications in various industrial areas. VR is not used on its own but it is interconnected with other advanced technologies, such as robotics and Internet of Things. In healthcare, several studies were performed that proved VR being a suitable means for treatment of certain health problems or an additional tool, for example in pain alleviation. Finally we discuss how VR can be used in university education not only as a tool for viewing 3D objects and scenes passively, but also as a tool for active interaction and execution of defined tasks.

During the last decade, we have experienced fast development of virtual reality technology combined with various sensors and applications in different fields. The devices and applications are more easily accessible for the broader public. In this article, we describe a feasibility study of an affordable personalized, immersive VR motor rehabilitation system with full body tracking. While virtual reality rehabilitation is a relatively new field, several applications were already proven more effective than traditional rehabilitation programs. The applied methods utilize VR headset HTC Vive and HTC trackers together witch inverse kinematics algorithms to provide full body tracking. For that, we provide a framework for individual body calibration. The main result of the study is a developed virtual environment with guided rehabilitation scenarios based on personalized body calibration. We have proven that this approach can be used in personalized rehabilitation programs.

The quality of life of the elderly is becoming more and more important. Prevention and early evaluation of falls in elderly people is a very important issue. Achieving the best comfort can be possible by contactless methods. The paper describes machine learning approach to a fall detection based on body posture classification using 8x8 pixel image acquired by Grid-EYE array temperature sensor. State-of-the-art computer vision tool - deep neural network - is used. The best result is achieved if classification into three or five classes is assumed. Even with such low resolution thermal image sensor, the final sensitivity and specificity of the class “laying” which corresponds to posture of a fallen person reaches 0.85 and 0.93, respectively.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This paper presents an analysis of selected social networking portals for science, research and innovation as the first step in development of a portal for social network of IT professionals in the Czech Republic. First, actual portal implementations of four portals dedicated to scientific communities of researchers are analyzed, compared from the perspective of provided functions, layout and their interdependence and the main features are summarized. Then, the most important recommendations for the portal being developed are presented. The last part of the paper introduces two concepts of structured profiles - knowledge profile and scientific profile - that are proposed in order to precisely and explicitly express knowledge and scientific alignment of an IT professional. These two profiles will make it possible to search and compare IT specialists, their groups and projects or pair the users to the demands on the market. The paper concludes with a list of typical use cases where the profiles will be utilized.

The paper presents briefly our educational activities in the area of assistive technologies. We have developed a special course on assistive technologies that uses in practical part project-based approach. We have established a specialized facility where the students can work on their projects. The facility simulates real environment of a smart home and a hospital room. The students can select a topic for a project from our proposals or they can come with their own ideas. In the paper we present results of several student projects that can be in the future used as practical applications for people with special needs. Some of them can be additionally used as games both for children and adults.

Healthcare applications involve complex structures of interacting processes and professionals that need to exchange information to provide the care services. In this kind of system, many different professional competencies, ethical and sensibility requirements, as well as legal frameworks coexist, and because of that, the information managed inside the system should not be freely accessed. On the contrary, it must be subject to very complex privacy restrictions. In the chapter, the authors describe a case study of a knowledge-based distributed system, the fundamental issues that must be considered in design of a distributed healthcare application. The K4CARE system is an example of an application to the medical domain of homecare assistance. Homecare involves professionals from different institutions (hospital, social workers, etc.) that must interact around any particular patient, and which used to be located in different physical places having their own and independent information systems.

In this paper we present two developed multi-agent architectures. The first one models knowledge-based support of the eServices in the domain of home care, while the second one represents a general model that uses procedural knowledge in the form of organizational processes and formalized medical guidelines in a decision support system. We show that integration of both architectures is feasible and moreover, it can enhance functionality of the home care system.

The paper is focused on description of an ongoing project of a pilot study and implementation of services supporting work of care givers in the area of home care and running on mobile telephones or PDAs. This work is part of the K4Care EU project that is focused on development of a system for knowledge-based home care e-Services. Home care patients receive different services at home, like visits either from nurses, physicians or social workers. The flow of information among them about the state of the patient or the changes to be performed on treatments require direct communication among all the professionals, which is frequently quite difficult to organize, time consuming, and scarcely reliable. The K4Care platform is accessible using different ICTs. It is a web-based platform, and the services provided, are accessible by web browsers and wireless devices, such as mobile telephones or PDAs. With this integrated platform, the flow of information about the updated state of the patient among the different professionals can become timespace independent.

Our previous study was aimed at the classification of right index finger movement direction by means of the movement-related EEG signal. The EEG database we used was originally recorded for a physiological research; from our point of view it has one significant drawback: there is no continuous non-movement related (resting) EEG of sufficient length (≫10 sec) in the database. To overcome this limitation we decided to generate artificial resting EEG signal. This article describes method and process of non-movement related (resting) EEG signal generation along with the reached new classification results including the false alarm rate. Artificial EEG signal was generated by AR modelling. The AR model parameters were estimated from short segments (3 sec) of resting EEG already present in the database using the autocorrelation method. Owing to large intra- and inter-personal variability one set of parameters had to be estimated for each person and electrode. The artificial EEG was used for the classification with satisfactory classification results.

Modeling and analysis of behaviour by using data extracted from Internet of Things (IoT) sensors is an open area. We take Behaviour Informatics (BI) as a formal representation into account and describe the case study of the apartment monitored by IoT sensors. The case study targets persons who live home alone (e.g., elderly people) without assistants (nurses), or any roommates. We present the apartment as a directed multigraph and propose the model to deal with the conversion of transactional data coming from IoT sensors into behavioural feature space represented by behavioural vectors. Further, the article describes a few use cases which can occur in the apartment with installed sensors and explains how behavioural vectors are created. Last but not least, we present the high-level overview of the complex system for detection and evaluation of behaviour identified from data of IoT sensors.