A data analytics framework for physiological signals from wearable devices

Wearable devices have emerged as the most innovative opportunity to enable acquisition and quantification of physiological signals in real-world indoor or outdoor contexts. However, their use in research should be based on a reproducible analytics process, ensuring that all the critical steps in data collection and processing are managed in a reliable experimental setup. The aim of this thesis is to investigate the actual value and technical limitations of wearable devices for their use in a research context, such as physiological monitoring of sleep and crying states in infants, of parenting of typical or atypical children, synchrony in educational contexts, and of fatigue patterns in outdoor sport activity, e.g. skiing. The thesis describes an approach and solutions that aim to compensate the effects of such technical limits. Besides providing a set of appropriate signal processing algorithms, a real-life sensing architecture is designed and implemented enabling synchronized acquisition from multiple subjects and multiple sensors, including cardiac signals, electrodermal activity and inertial data streams. The signal processing pipeline and the real-life sensing architecture are merged in a unique data analytics framework (Physiolitix). The framework is validated on a fairly wide range of sensors, including medical quality multi-sensor smartwatches and smart textile garments applied in diverse research contexts. In particular, a calibration dataset is developed to compare wearable and clinical devices in an affective computing task. We found that wearables can be employed as a valid substitute for medical quality devices with the help of adequate signal processing and machine learning solutions.