Wearable Technologies and Sensors for Enhanced Physiological Monitoring in Healthcare and Sports

Technological innovations have reshaped healthcare and wellness, promoting proactive health management. Among these advancements, wearable devices (WDs) contribute significantly by enabling continuous, long-term monitoring of physiological parameters beyond clinical setting. This enhances health awareness and supports clinical decision-making with objective data. In sports, WDs facilitate real-time tracking of multiple physiological and biomechanical parameters, optimizing training and helping to prevent injuries. However, despite these benefits, real-world monitoring is not yet fully integrated into everyday practice due to persisting limitations, particularly in measurement accuracy and user comfort, which still need to be addressed. This PhD thesis focuses on the design, development, and validation of WDs for cardiac and respiratory monitoring across daily life, clinical, and sports settings. It highlights the importance of adapting WDs to each context, particularly in non-laboratory conditions. For daily life applications, inertial measurement unit (IMU) sensors were investigated for heart rate (HR) and respiratory rate (RR) monitoring. Sensor placement has been shown to significantly influence accuracy, with the mitral valve location providing the best performance for HR and RR monitoring. Moreover, while IMU-based methods have demonstrated promising performance for HR estimation, RR monitoring remains more challenging due to the higher susceptibility to motion artifacts and postural changes. To address these limitations, textile-based sensors have been investigated, providing improved RR estimation, even in dynamic conditions. In clinical settings, research demonstrated that IMU sensor placement and algorithm selection impact the classification of aortic stenosis. Sensors at the pulmonary and mitral valves yielded the highest accuracy (>95%), with support vector machines outperforming deep learning models. Similarly, heart rate variability analysis with IMUs demonstrated that performance depends on posture, with supine measurements yielding better results. In sports, motion artifacts pose a major challenge for respiratory monitoring. To address this issue, a smart facemask embedding a temperature sensor has been developed for breathing monitoring. Its working principle has been shown to mitigate the impact of motion, while its design minimizes the increase in airflow resistance typically associated with face masks, preserving athlete comfort. Testing confirmed its accuracy, with a mean absolute error below 1 breath per minute. Chest-worn devices, commonly used in sports due to their comfort, were also tested. Since they are prone to motion artifacts, an algorithm was developed to assess signal quality and identify unreliable breaths. This approach reduced the mean absolute percentage error by 30.7% during high-intensity interval training while excluding only 4.4% of detected breaths, demonstrating its effectiveness compared to the unprocessed data. In conclusion, leveraging IMU sensors, textile-based technologies and temperature sensing systems, this work investigates sensor design and configurations, their placement on the body and algorithms to improve the applicability and reliability of WDs in real-world scenarios. The work is organized into five chapters: Chapter 1 presents the background and establishes the research context; Chapter 2 explores WDs for cardiac and respiratory monitoring in daily life; Chapter 3 presents IMU-based solutions for cardiac monitoring in clinical applications; Chapter 4 investigates WDs for respiratory monitoring in sports; Chapter 5 summarizes the main findings and explores future perspectives in wearable monitoring systems.