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Precise heart rate (HR) estimation of individuals during physical exercise from PPG sensors using direct methods often produces erroneous output due to the occurrence of exceedingly high motion artifacts (MAs). The effect of MAs can be neutralized using non-direct algorithms such as optimization techniques using some a priori about MA. In this paper, we propose a dual-optimization technique using adaptive Fourier decomposition in conjunction with sparse spectral reconstruction (AFODSS) using a multiple measurement vectors (MMVs) model and a regularized bi-conjugate gradient focal underdetermined system solver (BCG-FOCUSS) algorithm. Using AFODSS, the average absolute error (AAE) of HR obtained on the dataset recorded from 23 subjects during their intense exercise is 1.35 beats/min. The BCG-FOCUSS algorithm used in this paper helps to speed up the convergence rate and reduce time complexity when compared with other FOCUSS algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a smart, automated heart health-monitoring (SAHM) device using a single photoplethysmography (PPG) sensor that can monitor cardiac health. The SAHM uses an Orthogonal Matching Pursuit (OMP)-based classifier along with low-rank motion artifact removal as a pre-processing stage. Major contributions of the proposed SAHM device over existing state-of-the-art technologies include these factors: (i) the detection algorithm works with robust features extracted from a single PPG sensor; (ii) the motion compensation algorithm for the PPG signal can make the device wearable; and (iii) the real-time analysis of PPG input and sharing through the Internet. The proposed low-cost, compact and user-friendly PPG device can also be prototyped easily. The SAHM system was tested on three different datasets, and detailed performance analysis was carried out to show and prove the efficiency of the proposed algorithm., (© 2020 Walter de Gruyter GmbH, Berlin/Boston.)