Improving heart variability and electrodermal activity measurements from wearable devices for fatigue monitoring

点击翻译按钮获取中文摘要

Wearable technologies are increasingly favoured for health and fitness tracking, yet ensuring the reliability of the data they provide, particularly in medical contexts, is paramount for a correct interpretation. This project focuses on two electrophysiological measurements, namely heart rate variability (HRV) derived from optical sensors collecting photoplethysmography (PPG) signals, and electrodermal activity (EDA), both offering insights into autonomic nervous system activity, including sympathetic and vagal responses. However, these signals are prone to variation due to environmental factors such as ambient light and temperature, physiological factors like age, sex, and skin colour, as well as recording conditions including body position and sensor placement. Consequently, PPG signals frequently misidentify heartbeats, rendering them unreliable for HRV analysis which necessitates accurate and continuous heartbeat detection. Similarly, EDA is highly susceptible to motion artifacts, demanding meticulous analysis for dependable conclusions. The project endeavours to employ robust signal processing and estimation techniques to derive accurate vital and cardiovascular data from HRV and EDA measurements, while also quantifying their uncertainties. Currently, the lack of accompanying uncertainty estimates in wearable device data impedes its interpretation, underscoring the significance of this research endeavour. This holistic approach to physiological monitoring holds promise for advancing personalized healthcare and facilitating early detection of health conditions.