National Repository of Grey Literature 80 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Application for mastering of handwriting
Gradoš, Matej ; Gavenčiak, Michal (referee) ; Mekyska, Jiří (advisor)
Writing is an activity that accompanies us almost every day from a very young age. Children begin to familiarize themselves with the proper grip of a writing tool as early as preschool, primarily through activities related to drawing. Upon entering the early grades of elementary school, children start writing and practice it in special notebooks called „písanka“. Mastering this skill requires a strong connection between fine motor skills and perceptual abilities. The aim of this bachelor’s thesis is to transfer writing practice to an online environment and design an application for Apple iPad tablets with the Apple Pencil stylus, which enables children to engage in self-study through interactive means, ranging from tracing curves and basic letter elements to writing complete words. By creating a database of exercises, we can supplement traditional printed exercise book and thus monitor not only the result of handwriting but also its entire progression, capturing data on pen movement, tilt, and pressure The data obtained this way serves as an excellent tool for improvement and gaining confidence in writing.
Virtual reality as a tool for therapy in medicine
Pecháček, Vilém ; Mekyska, Jiří (referee) ; Mucha, Ján (advisor)
Virtual reality is increasingly the subject of scientific studies dealing with the therapy of neurodegenerative diseases, and its use in combination with conventional therapy appears to be beneficial. The aim of the work is a research of the available literature on this topic and an analysis of commercially available headsets and their technologies. Another of the objectives of the work is the design and implementation of an application for the therapy of motor symptoms of Parkinson's disease with an emphasis on data collection. The application will be developed in the Unreal Engine 5 environment for the Meta Quest 2 headset.
Analysis of speech disorders in patients with a high risk of developing Lewy body diseases
Novotný, Kryštof ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
Lewy bodies diseases (one of the most common neurodegenerative disorders) have the same pathological basis, but the individual representatives differ in their clinical manifestations. Different diseases affect the mental or physical side of the patient to a greater or lesser extent. This work assumes that thanks to the acoustic analysis of speech, it is possible to distinguish individual diseases from one another, because the disorders of the cognitive and motor aspects of a patient reflect in speech in specific ways. The thesis aims to describe the clinical features of the main representatives of the Lewy bodies diseases, to investigate their impact on speech, to propose characterizing acoustic parameters and then to compare their discriminative power. Speech recordings from the CoBeN and preLBD databases are used as input data for the proposed algorithm. Descriptive statistics, Mann-Whitney U test, FDR correction and XGBoost machine learning model using stratified cross-validation and balanced accuracy are used for subsequent evaluation. The result are scripts for the automated calculation of speech parameters from the database and their evaluation. The results of the analysis prove that the selected diseases can really be distinguished from each other and from a healthy control based on the manifestations in speech, already in the prodromal stages.
Development of features quantifying respiratory dysfunctions in Parkinson’s disease patients
Cvetler, Dominik ; Mekyska, Jiří (referee) ; Kováč, Daniel (advisor)
In the beginning of the thesis, Parkinson's disease and hypokinetic dysarthria are briefly described, which have a negative effect on speech production and cause breathing problems during speech in sick patients. The aim of the thesis is to create an algorithm for automated detection of breaths and the design of parameters for the quantification of respiratory disorders in patients with Parkinson's disease. In the MATLAB environment, the recordings of the researched subjects were processed and an algorithm was created for the detection of breaths, which used the logistic regression method. Based on the predicted breaths, proposed parameters were extracted from the recordings, which were then statistically analyzed and compared in healthy controls and patients with Parkinson's disease. By using a machine learning model, it was possible to predict the clinical data of patients from the proposed parameters to a certain extent. The average accuracy of the model for predicting puffs was 0.85. Of the 14 proposed parameters, 6 were suitable for quantifying respiratory disorders associated with hypokinetic dysarthria. The result of the work is a functional algorithm for the automated detection of breaths in the speech signal and proposed parameters that could be useful for the quantification of respiratory disorders in patients with Parkinson's disease.
Sub-types of hypokinetic dysarthria in patients with moderete Parkinson's disease
Adamják, Adam ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
This final thesis deals with the research of Parkinson's disease, hypokinetic dysarthria, and acoustic and statistical analyses. Hypokinetic dysarthria is a speech disorder that is a typical manifestation of Parkinson's disease, a neurodegenerative disease that affects approximately 2% of the population over the age of 65. The aim of this work is to reveal the subtypes of hypokinetic dysarthria, based on clinical parameters, acoustic analysis, and statistical analysis. In the acoustic analysis, parameters that examine the area of phonation, prosody, articulation, and speech tempo have been implemented. Subsequently, a statistical analysis was processed, thanks to which it was possible to reveal the subtypes of hypokinetic dysarthria.
Remote Parkinson's disease monitoring system
Podlužanský, Pavel ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
Parkinson's disease is an incurable disease, but with appropriate monitoring, the condition of patients can be significantly improved. The thesis deals with the creation of a system for monitoring Parkinson's disease. The system can save both neurologists and patients valuable time, and at the same time, it can allow neurologists to take care of more patients. The system is implemented, secured and tested as designed. The system can reliably evaluate the results of examinations from patients and send them to neurologists. Individual examinations belong to different categories. Patients are monitored based on movement, questionnaire or speech examinations.
Differential analysis of online handwriting versus pen and paper handwriting.
Plaček, Ivo ; Mekyska, Jiří (referee) ; Mucha, Ján (advisor)
The presented diploma thesis presents a basic introduction with the writing and its development, writing disordes, used devices for online writing and their software. It also contains the collected data, which is then analyzed to determine the difference between the tested devices. The main part of the work is devoted to the evaluation of the differences between writing on paper and digitizing tablets, both from the point of view of the differences in individual parameters, and also from the point of view of the influence of the differences in these parameters by the age groups of the respondents and their gender.In the course of the work, a comparison of individual parameters was performed, a T-test was performed between parameters in individual groups created according to the age of the respondents, but also between their genders. Subsequently, a correlation analysis was carried out and, at the end of the work, an evaluation of the results, when differences were found in the speed of writing, its width and the pressure on the pen when writing on individual devices..
Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries
Mikulec, Marek ; Mekyska, Jiří ; Galáž, Zoltán
Parkinson’s disease is accompanied by sleep disorders in most cases. Therefore patients with Parkinson’s disease could be identified according to proper sleep metrics. The study aims to train a classifier and identify proper sleep metrics, that could distinguish patients with Parkinson’s disease from subjects in control group based on data from actigraphy and sleep diaries. Study sample consisted of 23 patients with probable Parkinson’s disease and 71 control subjects resulting in 654 nights of actigraphy and sleep diary data, with 26 unique features per night. XGBoost classifier was trained to distinguish the groups, scoring 80% accuracy and 52% F1 on test data. Actigraphy based parameters targeted on wake analysis during sleep were marked as most important. The study provided classifier and obtained the most important parameters to identify patients with Parkinson’s disease based on actigraphy and sleep diary data.
Discovering relationship between graphomotor difficulties and isochrony in childrens online handwriting
Gavenčiak, Michal ; Zvončák, Vojtěch ; Mekyska, Jiří
Approximately 30–60 % of the time children spend in school is associated with handwriting. However, up to 30% of them experience graphomotor difficulties (GD), which lead to a decrease in their academic performance. Current GD diagnostic methods are not unified and show signs of subjectivity which can lead to misdiagnosis. This paper proposes novel handwriting features based on movement isochorny that enable computerised assessment of GD with approximately 20 % error.
Virtual reality as a tool for diagnosis and therapy in medicine
Kadlec, Jiří ; Mekyska, Jiří (referee) ; Mucha, Ján (advisor)
The use of virtual reality (VR) in the diagnosis and treatment of severe neurodegenerative or neurodevelopmental diseases is a potential alternative to standard methods and is now the subject of many studies and research. One of the objectives of the thesis is a detailed research and analysis of this usage. Another objective is to research and analyze the options of developing VR applications. The main objective of the thesis is the design and implementation of VR application for therapy and diagnosis of patients with Parkinson's disease. The application contain an adaptive environment and three designed exercises based on existing methods for diagnosis and therapy of patients with PD. Among other things, the application also allow you to store exercise data (such as position and rotation data of controls etc.). The implementation was done in the Unity engine with C# as a programming language, with an emphasis on patient adaptation and minimizing the development of VR disease.

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