Wearables for Health - Turning physiological signals into clinical insight

Abstract

Physiological signals measured by wearables and mobile health devices hold great promise for enabling health monitoring in daily life, and informing clinical decision-making. I will suggest that three questions hold the key to clinical impact: what information do these signals contain, how can it be reliably extracted, and how should it be used in practice?

In this talk, I will explore each of these questions through recent studies. First, I will show how modelling and analysis of wrist photoplethysmography (PPG) signals can reveal the cardiovascular properties and contextual factors (such as posture and sensor placement) that shape these signals. Second, I will discuss methods to extract information robustly, including benchmarking open-source algorithms for beat detection, developing a state-of-the-art PPG beat detector, and strategies for identifying reliable data segments. Third, I will present lessons from clinical applications, including our finding that atrial fibrillation diagnosis from single-lead ECGs collected on mobile devices is only moderately reliable, highlighting the importance of quality control and the potential of human-ML collaboration.

Together, these findings demonstrate opportunities and challenges of translating wearable data into clinical impact, and point to key considerations in the design of wearable systems for healthcare.