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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.

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.

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.

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.

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.

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.