UC Davis

We present an algorithm for live tracking of quasi-periodic faint signals in non-stationary, noisy, and phase-desynchronized time series measurements that commonly arise in embedded applications, such as wearable health monitoring. The first step of Rt-Traq is to continuously select fixed-length windows based on the rise or fall of data values in the stream. Subsequently, Rt-Traq calculates an averaged representative window, and its spectrum, whose frequency peaks reveal the underlying quasi-periodic signals. As each new data sample comes in, Rt-Traq incrementally updates the spectrum, to continuously track the signals through time. We develop several alternate implementations of the proposed algorithm. We evaluate their performance in tracking maternal and fetal heart rate using non-invasive photoplethysmography (PPG) data collected by a wearable device from animal experiments as well as a number of pregnant women who participated in our study. Our empirical results demonstrate improvements compared to competing approaches. We also analyze the memory requirement and complexity trade-offs between the implementations, which impact their demand on platform resources for real-time operation.