An Overview of Wearable Photoplethysmographic Sensors and Various Algorithms for Tracking of Heart Rates

It is very challenging to estimate the accurate heart rate/beat during intense physical activities due to corruption of motion artifacts (MAs). However, it is difficult to reconstruct a clean signal and extract heart rate/beat from contaminated photoplethysmography (PPG) signals. It was also observed that various algorithms have been developed for use in the detection of heart rates during physical activities by reconstructing the contaminated PPG signals to clean PPG signals. Against this backdrop, an overview of the various algorithms was conducted with their results from various works. These results are such that the motion-tolerant adaptive algorithm indicated high agreement and high correlation of more than 0.98 for heart rate (HR) and 0.7 for pulse oxygen saturation (SpO2) extraction between measurements by reference sensors and the algorithm. In addition, the distortion rates were reduced from 52.3% to 3.53%, at frequencies between 1 Hz and 2.5 Hz, when the two-dimensional active noise cancellation algorithm was applied representing daily motion such as walking and jogging. The correlation coefficient between the power spectral densities of the reference and reconstructed heart-rate time series was found to be 0.98, which showed that the spectral filter algorithm for motion artifacts and heart-rate reconstruction (SpaMA) method has a potential for PPG-based HR monitoring in wearable devices for fitness tracking and health monitoring during intense physical activities. The experimental result of the single-notch filter and ensemble empirical mode decomposition (NFEEMD) algorithm using the Pearson correlation was 0.992 which illustrated that the NFEEMD algorithm is not only suitable for HR estimation during continuous activities but also for intense physical activities with acceleration. Other algorithms suitable for HR estimation during physical activities include the time–frequency spectrum for the detection of motion artifacts (TifMA) algorithm, novel time-varying spectral filtering algorithm, noise-robust heart-rate estimation algorithm, real-time QRS detection algorithm, and many other algorithms in this regard.