ISEP - DM – Engenharia de Inteligência Artificial
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Browsing ISEP - DM – Engenharia de Inteligência Artificial by advisor "Conceição, Luis Manuel Silva"
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- Ensemble AI Solutions for Personalized Sleep Monitoring Using Wrist-worn WearablesPublication . SILVA, VASCO ANTÓNIO PORTILHO CARVALHO DA; Conceição, Luis Manuel SilvaSleep disorders, including insomnia and sleep apnoea, affect a significant proportion of the global population and are closely linked to cardiovascular, metabolic, and mental health conditions. Accurate and long-term monitoring of sleep is therefore a public health priority, as early detection and personalised management can substantially improve quality of life and reduce healthcare costs. This dissertation explores how wrist-worn wearable devices, combined with advanced machine learning and explainable artificial intelligence (XAI) techniques, can enhance the monitoring and analysis of sleep. While polysomnography (PSG) remains the clinical gold standard for sleep assessment, its cost, intrusiveness, and limited scalability restrict its long-term and widespread applicability. To address these limitations, this work proposes an integrated framework that leverages multimodal data, including photoplethysmography (PPG) and accelerometry, for automatic sleep stage classification and the detection of sleep apnoea. The system incorporates ensemble machine learning models to generate high-quality, personalised insights into sleep quality. Furthermore, explainability is ensured through the application of XAI methods, namely SHAP and LIME, enabling healthcare professionals and end-users to understand and trust model predictions. Experimental validation was conducted using multiple publicly available datasets, demonstrating the system’s robustness and generalisability across heterogeneous populations. Ultimately, this research contributes to the development of transparent, non-invasive, and scalable sleep monitoring solutions. It lays the groundwork for real-world applications in personalised healthcare and the early detection of sleep disorders, promoting better clinical decision-making and long-term well-being.