Photoplethysmography and Inertial Sensors in Wearable Devices for Healthcare: Multimodal Signal Processing for Increasing Accuracy

Multimodal signal processing is a technique by which signals from different physical domains are processed together in order to aid or improve the detection or measurement of quantities of interest. In this chapter we review a few key techniques that combine photoplethysmography (PPG) signals, that is, a non-invasive, optical measure of the peripheral blood flow commonly employed to measure parameters such as blood oxygenation (SpO2), heart rate (HR), or heart rate variation (HRV), and movement data coming from inertial sensors. This combination of signals is often employed because movement, especially in the limbs, greatly affects blood flow, and hence the PPG signal. We show that a number of techniques can be applied so that the signal from the inertial sensors can be used to clean out the so-called motion artifacts (MA) from the PPG signal, enhancing the accuracy of the HR information that can be extracted from it. The two signals can also be used together to improve the classification accuracy of the activities being performed, and this can in turn be used again to improve e.g., MA rejection, or to just obtain better estimates of the amount and type of activity a person is doing, which can be helpful in healthcare and/or nursing environments.