Feature-Based Correlation and Topological Similarity for Interbeat Interval Estimation Using Ultrawideband Radar.

The objectives of this paper are to propose a method that can accurately estimate the human heart rate (HR) using an ultrawideband (UWB) radar system, and to determine the performance of the proposed method through measurements. The proposed method uses the feature points of a radar signal to estimate the HR efficiently and accurately. Fourier- and periodicity-based methods are inappropriate for estimation of instantaneous HRs in real time because heartbeat waveforms are highly variable, even within the beat-to-beat interval. We define six radar waveform features that enable correlation processing to be performed quickly and accurately. In addition, we propose a feature topology signal that is generated from a feature sequence without using amplitude information. This feature topology signal is used to find unreliable feature points, and thus, to suppress inaccurate HR estimates. Measurements were taken using UWB radar, while simultaneously performing electrocardiography measurements in an experiment that was conducted on nine participants. The proposed method achieved an average root-mean-square error in the interbeat interval of 7.17 ms for the nine participants. The results demonstrate the effectiveness and accuracy of the proposed method. The significance of this study for biomedical research is that the proposed method will be useful in the realization of a remote vital signs monitoring system that enables accurate estimation of HR variability, which has been used in various clinical settings for the treatment of conditions such as diabetes and arterial hypertension.

Estimation of muscle fatigue by ratio of mean frequency to average rectified value from surface electromyography.

A new method to estimate muscle fatigue quantitatively from surface electromyography (EMG) is proposed. The ratio of mean frequency (MNF) to average rectified value (ARV) is used as the index of muscle fatigue, and muscle fatigue is detected when MNF/ARV falls below a pre-determined or pre-calculated baseline. MNF/ARV gives larger distinction between fatigued muscle and non-fatigued muscle. Experiment results show the effectiveness of our method in estimating muscle fatigue more correctly compared to conventional methods. An early evaluation based on the initial value of MNF/ARV and the subjective time when the subjects start feeling the fatigue also indicates the possibility of calculating baseline from the initial value of MNF/ARV.