Periodic average magnitude difference function for remote heart rate monitoring

Abstract

With the increasing attention on remote monitoring of human heart rate by radar, there is a need to develop a method that can estimate heart rate quickly and reliably. In this study, a new estimation method using a periodic average magnitude difference function (PAMDF) is proposed to estimate the heart rate from the radar signal. PAMDF advances the classical average magnitude difference function (AMDF) with the help of maximum likelihood (ML) theory. It operates in the time domain and estimates the heart rate by calculating the signal magnitude difference between all heartbeat periods. The proposed technique is more accurate than AMDF and has a similar performance to the ML estimation, while maintaining the low complexity advantage of AMDF. The algorithm was validated using radar data from two publicly available datasets.

Performance Analysis of the Maximum Likelihood Estimation of Signal Period Length and Its Application in Heart Rate Estimation with Reduced Respiratory Influence

“…The periodic signal model (2) includes P s + 1 unknown parameters, where P s is unknown itself. Therefore, it constitutes an irregular estimation problem, whose maximum likelihood estimator is biased, and the classical Cramer-Rao bound does not exist [21]. To accurately describe the performance of the estimator, we established a regularized model similar to that of Equation ( 2) with a fixed number of unknown parameters:…”

“…On the other hand, a period length estimation method of an unknown waveform signal in the time domain based on the maximum likelihood theory originally designed for pitch period estimation by Wise et al in [19] was introduced in radar-based monitoring by Conte et al [20]. The estimator makes no assumption about the signal waveform in the time domain, which is suitable for the heartbeat signal [21].…”