Ing. Zdeněk Horčík

The motivation of incoming students, their identification with the field of study and their involvement in the events at the faculty is an important factor that determines the future success of their studies. The aim of the paper is to present a motivational workshop, which is held annually for incoming students of the master's program Medical Electronics and Bioinformatics at FEE CTU in Prague in the last week before the beginning of the first semester. This workshop is organized as a four-day field meeting of students and teachers and thus enables the creation of social-work ties between teachers and students, which cannot be created on the faculty premises. The content of the workshop is lectures of teachers of selected subjects, in which students are acquainted with current topics of the field and with the initial knowledge expected, with the organization of master's studies, with the possibility of trips abroad and double-degree study. During the workshop, students also present their bachelor's theses, which allows them to get acquainted with each other's current projects and participate in the work of research groups at the faculty. An integral part of the workshop is a creative workshop, in which students in small groups design and implement various small biomedical devices, and whose goal is to motivate students to creative teamwork both in the short term and especially in the course of their studies.

The authors present a possibilities of using Arduino platform as an instrument of project based education.

The paper deals with an initial study of monitoring and classification of a tremor. The different types of tremor are currently classified according to clinical scales, unfortunately, the objective approach is still missing. The principle design of a measuring device is presented in the paper together with the first examples of obtained and processed signals. The device is based on the 3-axis accellerometer and 3-axis gyroscope chip (MotionTracking sensor MPU-6050).

Remote patient monitoring is gradually attracting more attention as the population in developed countries ages, and as chronic diseases appear more frequently in the population. Miniaturization in electronics and mobile technologies has led to rapid development of various wearable systems for remote monitoring of vital signs, supervision systems in home care, assistive technologies and similar systems. There is a significant demand for developing the necessary devices very rapidly, especially for shortening the way from an idea to a first function sample. This paper presents a solution for rapidly developing devices for telemedical applications, remote monitoring and assistive technologies. The approach used here is to design and realize a modular system consisting of input modules for signal acquisition, a control unit for signal pre-processing, handshaking of data communication, controlling the system and providing the user interface and communication modules for data transmission to a superordinate system. A description of specific applications developed on the basis of the system is also presented in the paper.

Model of the cardiovascular system is a mechanical simulator of a hemodynamic parameters of the human bloodstream. It is a system of tubes, valves, pumps and other elements, which are arranged to simulate a circulation of blood of a healthy human body. It is a possible to monitor a hemodynamic parameters on the model. Basic parameters included the blood pressure, cardiac output, pulse wave velocity and more. An essential part of the this system is a mechanical pump that simulate pulsatile blood flow emitted by healthy human heart. This paper describes the design and construction of the model, the control method of the mechanical pump and methods of the measurement of a hemodynamic parameters. The aim of the project is to create a faithful model of the cardiovascular system with the possibility of measuring hemodynamic parameters using a conventional monitoring devices.

Remote vital signs monitoring is attracting more and more attention as the population in developed countries is aging, and as the chronic diseases appear more frequently in the population. Smart mobile technologies and miniaturization in electronics have enabled fast development of systems for remote monitoring of vital signs. This paper presents a hardware solution of a mobile device for remote monitoring and shows that the mentioned issues can be addressed and efficiently solved. The project focused on long term measurement of heart rate and the Intelligent Primer Nurse project are introduced in the paper.

This paper deals with a comparison of k-Means and Bayes classifiers designed for classification of the human body motions classification. The main task of this paper is to compare the performance and the reliability of classifiers. The presented work is a part of research of relations between brain and muscle activity. The sensing of body motions is based on standard DV camcorders system. The procedures have no negative impact to brain activity (the tracking does not affect the measured EEG signals). Presented paper includes the description of observing, discerning and parameterization procedures and the discussion of motion classification. The results of classification accuracy are the part of this paper.

Presented contribution informs about the new subject X31ZLE Basics of medical electronics taught in Faculty of Electrical Engineering in Czech Technical University in Prague from winter semester of academic year 2008/2009. The main task of this subject is to show the fundamental principles applied within the modern medical devices and systems, esp. from the point of view of functional blocks and electronic circuits.

This paper deals with video based parameterization and classification of human body motions. The main task of this work is to develop and verify the procedures for observing of muscle and brain activity. The developed procedures have no negative impact to brain activity (the tracking does not affect the measured EEG signals). The procedures required only standard hardware equipment accessible on neurological laboratories. The body motions are non-contact sensed using a pair of standard DV camcorders. This work includes the description of observing, discerning and parameterization procedures and the discussion of motion classification. The set of classifiers - hierarchical clustering algorithm, recursive clustering algorithm, k-means classifier, Bayes classifier and classifier based on discrimination functions - was developed and implemented. The analysis of the classifiers properties was accomplished in this work. The accuracy of classification was tested for selected

This paper deals with a comparison of statistical and non-statistical classifiers for thumb motion classification. Presented work is a part of research of relation between brain and muscle activity. The thumb motion is represented by trajectory coordinates of special mark placed on the thumb. Motions are classified using k-Means classifier (non-statistical classification, no prior information is needed) and Bayes classifier (statistical models of classes are needed, classifier training is necessary). The efficiency of classifiers is evaluated using the standard HTK parameters. Real testing data includes more than 900 stationary states which are used for classifier testing. The classification results are compared for both methods.

The contribution deals with the study of cerrespondence between the muscle and the human brain activity. The description of the experiment is included. The main part describes the classification algorithms used for the thumb motion classification - hierarchical clustering, recursive clustering algorithm, k-Means clustering, Bayes classifier and discrimination functions. The statistic of classification results is included.

The contribution deals with the teaching of Medical equipment on FEE CTU in Prague. The contents of this course is included, especially the contents of lectures, seminars and laboratories.

This paper deals with the classification of three dimensional thumb motions. The thumb motion is parameterized tracing a special mark on the thumb, the coordinates of thumb trajectory are computed during the parameterization. The classification is based on the Bayes theorem. The normal distribution ofmotion parameters is assumed. The parameters of likelihood functions are estimated using the Maximum Likelihood Estimation. The presented classification is used in biomedical engineering for research on correlations between a human brain function and a muscle activity.

This paper deals with the classification of three dimensional thumb motions. The thumb motion is parameterized tracing a special mark on the thumb, the coordinates of thumb rajectory are computed during the parameterization. The discrimination functions used for the classification of thumb motion are derived in this paper. The discrimination functions are based on statistical data analysis. The functions are derived from the Bayes theorem. The normal distribution of motion parameters is assumed. The parameters of discrimination functions are estimated using the Maximum Likelihood Estimation. The presented lassification is used in biomedical engineering for research on correlations between a human brain function and a muscle activity.

This paper deals with three-dimensional thumb motion parameterization based on image processing. The finger motion is recorded using two camcorders shooting from two linear independent directions. This contribution describes the video records processing used for obtaining of thumb motion trajectory projection to the scan planes of camcorders. Subsequently there is explained the three-dimensional parameterization of thumb motion from the scan plane projections in this paper. The parameterized motions are used in biomedical engineering for research on correlations between a finger motions and a human brain activity.