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We describe an algorithm for estimating heart rate from an optically measured PPG signal when physical exercises are performed.
In this case, the PPG signal is contaminated by motion artifacts caused by hand movements, making it difficult to find its fundamental frequency that corresponds to the heart rate. To overcome the noise, a soft decision approach is taken, by which several candidates for the fundamental frequency of the PPG signal are extracted and assigned grades.
By appropriate grade weighting, the candidate having the maximal grade is selected. The presented algorithm is of low complexity and shown to provide good results. As such, it can be used in low-power portable devices for real time heart rate estimation.

Heart rate monitoring during physical exercise has become increasingly popular recent years. The monitoring is performed using wearable devices, which estimate heart rate in real time using photoplethysmographic (PPG) signals. These PPG signals are obtained by illuminating the skin by a light-emitting diode and measuring changes in light absorption by a photodiode. As the heart pumps blood through organs, volumetric changes of organs occur, reflected in periodic variations in measured light intensity. These variations are used in turn to determine the heart rate, usually in terms of beats-per-minute.