Abstract
Recently, photoplethysmography sensors in smart watches have been frequently used to monitor heart rate. Because a photoplethysmography sensor flashes light on the skin and measures the reflected light, the rate of flashing and sampling is directly related to its energy consumption. It is necessary to reduce the rate to extend the battery life of a smart watch. In this study, to satisfy sub-Nyquist sampling, real-time, and high accuracy requirements, we propose a novel heart rate tracking method that consists of online compressive covariance sensing, signal subspace tracking, and spectral peak tracking. The proposed method (average sampling rate: 1.4 Hz) showed better frequency tracking performance than the conventional spectral peak tracking algorithm (sampling rate: 10 Hz) in time-varying heart rate simulations, and a statistically significant difference between them was not observed in the real photoplethysmogram data. The trimmed average absolute error was 1.04 beats/min. As a result, the proposed real-time heart rate tracking method with sub-Nyquist sampling showed high accuracy.
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