This paper presents a real-time maximum-likelihood heart-rate estimator for ECG data measured via wearable textile sensors. The ECG signals measured from wearable dry electrodes are notorious for its susceptibility to interference from the respiration or the motion of wearing person such that the signal quality may degrade dramatically. To overcome these obstacles, in the proposed heart-rate estimator we first employ the subspace approach to remove the wandering baseline, then use a simple nonlinear absolute operation to reduce the high-frequency noise contamination, and finally apply the maximum likelihood estimation technique for estimating the interval of R-R peaks. A parameter derived from the byproduct of maximum likelihood estimation is also proposed as an indicator for signal quality. To achieve the goal of real-time, we develop a simple adaptive algorithm from the numerical power method to realize the subspace filter and apply the fast-Fourier transform (FFT) technique for realization of the correlation technique such that the whole estimator can be implemented in an FPGA system. Experiments are performed to demonstrate the viability of the proposed system.
This paper presents a real-time heart-rate estimator using the ECG data which are received via wireless bluetooth receiver and measured with wearable electrodes made of steel textile in a wearing system. The measured ECG signals, because of the movable electrodes and the resulting unfixed contacts, demonstrate high baseline wandering phenomena and high noisy aberrations especially when the wearing person is in motion, making the heart-rate estimation a sophisticated work. To conquer this problem, the presented heartrate estimator first uses the subspace technique to remove the baseline wandering in ECG signals, and then applies the adaptive notch filter (ANF) technique to obtain the heartrate estimate. Experiments for ECG data from the person with initially sitting still through walking steadily to jogging, demonstrate that the presented method obtains heartrate estimates which satisfactorily reflect the motion status of the person. The vital system embedded with the capability of real-time heart-rate estimation makes it highly suitable for applications of remote healthcare and wellness.
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