Accurate Detection of Heart Rate and Blood Oxygen Saturation in Reflective Photoplethysmography

In recent years, the demand for wrist wearable devices to monitor continuously critical physiological parameters in real time that are limited by designated hospital monitoring equipment is steadily increasing. In the medical field, one of the main issues that wearable devices could sufficiently address is the pervasive monitoring of vital signs and the corresponding health status assessment of the rapidly growing elderly population in real time. Main advantages in the adoption of wearable devices for the real time monitoring are the significant decrease of the cost both for the health system and subsequently the patient as well as the dramatic decrease of the waiting time in the hospital emergency rooms.Reflectance pulse oximetry being the right mode to be used at the wrist for measurements such as Heart Rate (HR), Peripheral Capillary Oxygen Saturation (SpO2) and Respiratory Rate (RR) imposes many technical challenges with its excessive sensitivity to all types of entailed artifacts due to arm/hand/body motions to be amongst the major ones.This work introduces a low-power wrist wearable device comprising a Photoplethysmography (PPG) array sensor special extraction algorithms to estimate HR and SpO2 parameters and a Multiple Linear Regression model, which after training performs considerable reduction of the imposed Motion Artifacts (Mas) thus enabling more accurate reading outputs.