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Petra Maresova,1 Lenka Komarkova,2 Jiri Horak,3 Ondrej Krejcar,1 Pavel Kukuliac,3 Lucie Vitkova,4 Miroslav Joukl,4 Zuzana Truhlarova,5 Signe Tomsone6 1Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, 500 03, Czech Republic; 2Faculty of Management, Prague University of Economics and Business, Jindrichuv Hradec, 37701, Czech Republic; 3Faculty of Mining and Geology, VSB—Technical University of Ostrava, Ostrava, 70800, Czech Republic; 4Philosophical Faculty, University of Hradec Kralove, Hradec Kralove, 500 03, Czech Republic; 5Faculty of Education, University of Hradec Kralove, Hradec Kralove, 500 03, Czech Republic; 6Faculty of Rehabilitation, Riga Stradins University, Riga, LV-1007, LatviaCorrespondence: Ondrej Krejcar, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove, 500 03, Czech Republic, Tel +420737928745, Email ondrej.krejcar@uhk.czBackground: Maintaining mobility is fundamental to active aging, allowing older adults to lead dynamic and independent lives. The perception of mobility among older adults significantly impacts their overall well-being and quality of life. Given the aging population, mobility has become an increasingly pressing issue.Aim: This study focused on the perception of urban neighborhoods, including considerations of urban tissue (crossings and sidewalk maintenance), urban scenes (benches and traffic), and safety (fears and street lighting quality). We investigated the differences in the perception of the surroundings of residences by urban and rural seniors concerning their demographic and social characteristics and environmental determinants.Methods: A quantitative study design utilizing a questionnaire survey was employed. Data were collected mainly through face-to-face interviews in the field (PAPI) and via an online questionnaire (CAWI). The final sample comprised 525 participants. Hypotheses regarding the influence of gender, age, social status, level of physical activity, degree of urbanization, and region on environmental perception were tested using ordinal regression.Results: The hypothesis regarding the dependence of the perception of the surroundings on the level of urbanization was confirmed; that regarding the dependence of the perception of the residence surroundings on seniors’ age was not confirmed. The other hypotheses were partially confirmed. For the seven investigated environmental attributes, gender was significant in two cases, social status and physical activity in three cases, and region in four cases.Conclusion: While most studies have focused on urban settings, this study highlights the situation in rural municipalities. Substantially worse pedestrian conditions in availability of pedestrian crossings, benches, and lighting were recognized in rural municipalities versus cities. Understanding the complexity of mobility and the spatial locations relevant for older persons concerning potential barriers and facilitators for mobility aids in planning and adapting neighborhood environments to promote active and healthy aging in place.Keywords: seniors, mobility, walking, perception, survey

Petra Maresova,1 Jan Hruska,1 Blanka Klimova,2 Sabina Barakovic,3 Ondrej Krejcar4 1Department of Economics, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove 500 03, Czech Republic; 2Department of Applied Linguistics, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove 500 03, Czech Republic; 3Faculty of Transport and Communications, University of Sarajevo, Sarajevo 71000, Bosnia and Herzegovina; 4Center for Basic and Applied Science, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove 500 03, Czech RepublicCorrespondence: Ondrej KrejcarCenter for Basic and Applied Science, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62/26, Hradec Kralove 500 03, Czech RepublicTel +420 777484280Email ondrej.krejcar@uhk.czAbstract: Nowadays, the population is rapidly ageing because of increasing life expectancy and decreasing birth rates. Thus, the purpose of this systematic review is to prepare a comprehensive overview which identifies the activities of daily living (ADLs) that are gradually reduced among patients with dementia, as well as explore the therapies applied in relation to dementia and how they effectively improve the quality of life (QoL) of patients and caregivers. Furthermore, we aim to summarise the ADL activities influenced by therapies and examine the treatment costs and care for patients so that recommendations for research and development (R&D) can be made to improve both the QoL of people with dementia and cost-saving measures. The research focuses on four selected neurodegenerative diseases: Alzheimer, Parkinson, vascular dementia, and amyotrophic lateral sclerosis. Therefore, the peer-reviewed English written articles from 2014 to 2019 were searched between September 1 and December 13, 2019. Twenty-seven papers were included in the analysis. The results show that essential assistance occurs in connection with activities: eating, drinking, dressing, bathing, personal hygiene, use of the toilet, and transport. By contrast, shopping or cleaning is not addressed as much. A lower ability to take care of oneself is connected with poor patient health and higher social care costs because the patient requires care from external sources, such as home aid or nurse visits. The challenge that remains is to shift new knowledge from scientific disciplines and connect it with the needs of patients to remove legitimate barriers and increase the acceptance of new solutions by popularisation. Additionally, regarding the burden on caregivers, it would be appropriate to promote this area of education and employment so that family members can use formal caregivers, ensuring them free time and much-needed rest.Keywords: costs, neurodegenerative diseases, quality of life, activities of daily living

Jakub Jirka,1 Michal Prauzek,1 Ondrej Krejcar,2 Kamil Kuca2,3 1Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB – Technical University of Ostrava, Ostrava Poruba, Czech Republic; 2Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czech Republic; 3Biomedical Research Center, University Hospital Hradec Kralove, Hradec Kralove, Czech Republic Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms.Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs.Results: The final application of GP–SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector.Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm’s classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS. Keywords: genetic programming, adaptive segmentation, SVM, fractal dimensions, EEG