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

可穿戴技术越来越受到健康和健身跟踪的青睐，但确保其提供的数据的可靠性，特别是在医疗环境中，对于正确解释至关重要。该项目侧重于两种电生理测量，即心率变异性（HRV），来自收集光体积脉搏波（PPG）信号的光学传感器，以及皮肤电活动（EDA），两者都提供了对自主神经系统活动的见解，包括交感神经和迷走神经反应。然而，这些信号很容易因环境因素（如环境光线和温度）、生理因素（如年龄、性别和肤色）以及记录条件（包括身体位置和传感器位置）而发生变化。因此，PPG信号经常错误识别心跳，使其不可靠的HRV分析，这需要准确和连续的心跳检测。同样，EDA也很容易受到运动伪影的影响，需要细致的分析才能得出可靠的结论。该项目努力采用稳健的信号处理和估计技术，从心率波动和EDA测量中获得准确的生命和心血管数据，同时量化其不确定性。目前，在可穿戴设备数据中缺乏伴随的不确定性估计阻碍了其解释，强调了这项研究工作的重要性。这种生理监测的整体方法有望推进个性化医疗保健和促进早期发现健康状况。

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.