This article describes a novel methodology to balance computational power and estimation accuracy for the application of robust heartrate (HR) monitoring through leveraging particle filters. We formulate the power-accuracy trade-off as the number of particles in the particle filter framework, where a higher number of particles leads to higher computational resolution and accuracy at the cost of higher computational power. Our particle filter-based HR monitoring technique can be applied to a variety of physiological signals including but not limited to electrocardiogram (ECG) and photoplethysmogram (PPG).