Short abstract The complex infrastructure of interconnected medical devices and systems had evolved over the last several decades to provide accurate, continuous, and reliable support of human health and wellbeing. The healthcare IoT monitors, consults, and delivers the service to people at different scales, from wristbands counting the steps and calories, to multimodal visualization for cancer therapy. In this tutorial, the general approach to medical IoT network will be presented, and the main toolboxes will be described. A brief overview of biosignals will be given to demonstrate the variety of the input data fro medical systems. The classification of biomedical systems will be provided with examples of recent developments in biosystems for diagnosis, monitoring, visualization, and others. Machine learning approach to the diagnosis will be discussed in the second part of the tutorial, and the signal analysis methods for feature engineering will be briefly summarized.

Bio Anton Popov is an associate professor of Electronic Engineering Department of Igor Sikorsky Kyiv Polytechnic Institute (Ukraine), He is a lecturer for the courses "Theory of signals", "Biomedical Electronic Systems", "Digital processing and analysis of biomedical signals and images". He is also the head of the Biomedical Electronics and Signal Analysis Group of Kyiv Polytechnic Institute, AI/Deep Learning technical lead in the Ciklum company (UK), and IEEE Senior Member. Anton Popov authored 150 publications in peer-reviewed journals and conference proceedings and was a supervisor for more than 60 bachelor, master, and PhD students. His research interests include applications of signal processing methods to the analysis and interpretation of biomedical signals and images. Currently, his group is working on epileptic seizure prediction based on electroencephalograms and cardiorythmograms, techniques for quantification of cognitive workload and emotions, recognition of imaginary movements, muscle synergies detection, stabilography, as well as other biosignals and medical images for the diagnosis of diseases and evaluating the person's physical conditions.

Inertial Suit contains biomechanical model of human body that is fed real-time data from live actor from 17 Inertial Measuring Units (IMUs) located on actor's body. It's main purpose is to do Motion Capture of human body.

It is a robust research and training tool with multiple applications: from simple movement recording through analyzing biomechanical aspects like angles and accelerations of specific body segments, detecting defined events, searching for problems in gait of sportsmen - to sophisticated group training systems immersed in Virtual Reality.

In our workshop session we want to show full body recording and potential measurements and analysis. We will show how system can warn about certain defined thresholds being crossed (function called bio-feedback). Discussion may cover potential applications in participant's field.

Eryk J. Lipinski has 20 years of experience with GNSS satellite-based positioning and inertial stabilization and 3D-orientation. He runs solution center company GPS.PL and delivers Motion Capture solutions to customers in fields of soldier training and simulation, computer game manufacturing and sports. He is Certified Running Gait Analyst and Trimble Certified Mapping Trainer.

Krzysztof Nowak is a technician at GPS.PL with experience in testing and configuration of MoCap systems.

Information extraction from images is a rapidly growing research field. Nowadays, deep learning (DL) methods are demonstrating remarkable results in many computer vision (CV) problems like object detection, face and text recognition, action recognition, object recognition and object tracking. However, such methods require high amount of training data and a lot of computational resources like GPUs or even clusters of hardware units. Moreover, another drawback is lack of scene geometry information utilized in DL models, which may degrade the expected results. In contrast, classical methods are based on feature crafting and a batch of user defined parameters, which often leads to unstable results. In this tutorial, we will discuss several typical CV problems and their classical and DL solutions. Comparative analysis will allow to give a clear picture of what group of methods should be applied and when. In particular, we are going to cover problems of object detection, multi-view matching, object tracking and simultaneous localization and mapping (SLAM).

We would like to cordially invite all participants to join the presentation about happiness in our lives, which shows various ways of achieving happiness and getting away from stress.

Good health and feeling of happiness can be measured by different parameters such as, for example, physiological homeostasis (homeostatic equilibrium), psychological wellbeing or the social survey. In order to deal with it philosophers and social psychologists carry out research into a good balance between positive and negative stress in our lives. In Poland a special "social diagnosis" and "onion theory of happiness" were presented by Prof. Janusz Czapiński.

According to Polish psychologist Prof. Kazimierz Dąbrowski, who was the author of the "theory of positive disintegration", before feeling undiluted happiness in one context we should experience an intermediate period of disintegration. It is good to accept this metamorphosis and after that we can enter the next level of our personality development.

In the experiment of Jane Eliot (antidiscrimination activist in the US) we will see how important empathy is in our lives. It can be shown in the case of racial discrimination - Jane Eliot's ‘Blue eyes - Brown eyes' experiment. Moreover, in the explanation given by a Polish neurobiologist Prof. Jerzy Vetulani we find out that a situation in the early childhood influences all human life and in fact this is the source of our optimism or pessimism.

Another strong factor of our health and happiness is our diet. Also, we know today that feeling of happiness can be strengthened through special exercises.