Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%

ROAD TRAFFIC ESTIMATION USING BLUETOOTH SENSORS

The Bluetooth standard is a low-cost, very popular communication protocol offering a wide range of applications in many fields. In this paper, a novel system for road traffic estimation using Bluetooth sensors has been presented. The system consists of three main modules: filtration, statistical analysis of historical, and traffic estimation and prediction. The filtration module is responsible for the classification of road users and detecting measurements that should be removed. Traffic estimation has been performed on the basis of the data collected by Bluetooth measuring devices and information on external conditions (e.g., temperature), all of which have been gathered in the city of Bielsko-Biala (Poland). The obtained results are very promising. The smallest average relative error between the number of cars estimated by the model and the actual traffic was less than 10%

Support for learning programming in early education using EduMATRIX

Obecnie bardzo duży nacisk w procesie edukacji kładziony jest na rozwijanie umiejętności logicznego i abstrakcyjnego myślenia, co jest niezbędne do nauki programowania. Powszechnie dostępne są systemy, które pomagają oswoić się z tym zagadnieniem już od najmłodszych lat. W niniejszej pracy przedstawiona została alternatywa dla takich aplikacji – bloczki EduMATRIX. Ich głównym atutem jest nauczanie zagadnień związanych z programowaniem bez konieczności przebywania przed ekranem komputera. Ponadto zaproponowano metodę walidacji użyteczności EduMATRIX oraz innych dostępnych pomocy dydaktycznych, która pozwoli na wskazanie skutecznej formy nauki dla młodych użytkowników.Currently, the development of the skills of logical and abstract thinking is empahasize in the edu-cation process. This is essential for learning programming. Systems that help to be familiar with this issue since an early age are widely available. In this paper an alternative for such applications is presented – EduMATRIX. Its main advantage is the teaching of programming without the need to stay in front of a computer screen. Moreover, a validation method of EduMATRIX and other available teaching aids, which will identify effective form of learning for young users, was proposed