Classification of the quality of wristband-based photoplethysmography signals

Wearable technologies have made ubiquitous, non-invasive continuous monitoring of vital signs outside of the clinical setting possible. Convenient and user friendly embedded sensors in wearable technologies such as wristbands or watch, unlike cumbersome electrocardiogram Holter monitors, have made long term monitoring possible in normal home/home-care setting. However, such vital sign monitors are highly susceptible to motion artifacts and hence the quality of signal suffers. In order to develop a reliable automated technique to estimate vital signs, it is necessary to understand and estimate the quality of the acquired signal. In this paper, we compare the characteristics of signal corrupted by motion artifact agains