Microwave reflectometric sensing combined with novel signal processing techniques can accurately reconstruct heart rate variability for stress evaluation without contact electrodes.
In this paper, we present two robust signal processing techniques for stress evaluation using a microwave reflectometric cardiopulmonary sensing instrument. These techniques enable the heart rate variability (HRV) to be recovered from measurements of body-surface dynamic motion, which is subsequently used for the stress evaluation. Specifically, two novel elements are introduced: one is a reconfiguration of the HRV from the cross-correlation function between a measurement signal and a template signal which is constructed by averaging periodic component over a measurement time. The other is a reconstruction of the HRV from the time variation of the heartbeat frequency; this is evaluated by a repetition of the maximum entropy method. These two signal processing techniques accomplish the reconstruction of the HRV, though they are completely different algorithms. For validations of our model, an experimental setup is presented and several sets of experimental data are analyzed using the two proposed signal processing techniques, which are subsequently used for the stress evaluation. The results presented herein are consistent with electrocardiogram data.
Nagae et al. (Wed,) studied this question.