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Machine Learning-Aided Monitoring and Prediction of Respiratory and Neurodegenerative Diseases Using Wearables
Skibińska, Justyna ; Esposito, Anna (oponent) ; Faundez-Zanuy, Marcos (oponent) ; Hošek, Jiří (vedoucí práce)
This thesis focuses on wearables for health status monitoring, covering applications aimed at emergency solutions to the COVID-19 pandemic and aging society. The methods of ambient assisted living (AAL) are presented for the neurodegenerative disease Parkinson's disease (PD), facilitating 'aging in place' thanks to machine learning and around wearables - solutions of mHealth. Furthermore, the approaches using machine learning and wearables are discussed for early-stage COVID-19 detection. Firstly, a publicly available dataset containing COVID-19, influenza, and healthy control data was reused for research purposes. The solution presented in this thesis is considering the classification problem and outperformed the state-of-the-art methods, whereas the original paper introduced just anomaly detection. The proposed model in the thesis for early detection of COVID-19 achieved 78 % for the k-NN classifier. Moreover, a second dataset available on request was utilized for recognition between COVID-19 cases and two types of influenza. The scrutinisation in the form of the classification between the COVID-19 and Influensa groups is proposed as the extension to the research presented in the original paper. The accuracy of the distinction between COVID-19 cases and influenza in the middle of the pandemic was equal to 73 % thanks to the k-NN. Furthermore, the contribution as the classification model of two aforementioned combined datasets was provided, and COVID-19 cases were able to be distinguished from healthy controls with 73 % accuracy thanks to XGBoost algorithm. The undeniable advantage of the illustrated approaches is taking into consideration the incubation period and contagiousness of the disease. In addition, some solutions for the detection of the aforementioned aging society phenomenon are presented. This study explores the possibility of fusing computerised analysis of hypomimia and hypokinetic dysarthria for the spectrum of Czech speech exercises. The introduced dataset is unique in this field because of its diversity and myriad of speech exercises. The aim is to introduce a new techniques of PD diagnosis that could be easily integrated into mHealth systems. A classifier based on XGBoost was used, and SHAP values were used to ensure interpretability. The presented interpretability allows for the identification of clinically valuable biomarkers. Moreover, the fusion of video and audio modalities increased the balanced accuracy to 83 %. This methodology pointed out the most indicative speech exercise – tongue twister from the clinical point of view. Furthermore, this work belongs to just a few studies which tackle the subject of utilising multimodality for PD and this approach was profitable in contrast with a single modality. Another study, presented in this thesis, investigated the possibility of detecting Parkinson's disease by observing changes in emotion expression during difficult-to-pronounce speech exercises. The obtained model with XGBoost achieved 69 % accuracy for a tongue twister. The usage of facial features, emotion recognition, and computational analysis of tongue twister was proved to be successful in PD detection, which is the key novelty and contribution of this study. Additionally, the unique overview of potential methodologies suitable for the detection of PD based on sleep disorders was depicted.
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Economic and social impacts of migration : The case of the United Kingdom
Béres, Dóra ; Karásek, Tomáš (vedoucí práce) ; Střítecký, Vít (oponent)
From 2015, a huge influx of refugees came from the Middle East, the Balkans, Central Asia and Africa to Europe. It is triggered by various persecutions, armed wars, economic impossibility. The majority of those arrived were refugees, those who had fled their country due to imminent threat or persecution and were even trying to reach the European continent at the risk of their lives. The others are economic immigrants who have migrated to the European Union in the hope of a better life - to work, study or reunite. The UK has been a major destination for both migrants and refugees for many decades. The dissertation draws attention to the complex effects of migration, with a particular focus on the host country, and highlights, especially in the UK, the need for migrants in an aging society in Europe, even if the public thinks otherwise. With the Brexit, the UK has exited the European Union, cut back on previous benefits for EU migrants and is opening up to former Commonwealth members as sending countries.
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