An instantaneous heart rate tracking method is presented to estimate beat-to-beat heart rates from wearable photoplethysmographic (PPG) sensors that are affected by nonstationary motion artifacts. Many state-of-the-art heart rate tracking methods estimate heart rates using an 8-second average instead of the instantaneous heart rates which especially fluctuate during exercises. In this paper, our novel technique showed accurate heart rate estimation from PPG signals acquired from wearable wrist and forehead devices which are affected by motion artifacts especially when subjects were running on a treadmill. The proposed method consists of three parts: 1) time-frequency spectrum estimation of PPG and accelerometer signals, 2) motion artifact removal by subtraction of the time-frequency spectra of the accelerometer signals from the PPG signals, and 3) postprocessing to reject remnant motion artifact affected heart rates followed by interpolation of removed heartbeats using a cubic spline approach. We present preliminary results compared with one of the most accurate state-of-the-art techniques [12]. The results were derived from two different datasets: IEEE Signal Processing Cup Challenge and our own dataset obtained from a wrist and a forehead PPG sensor, respectively, with subjects running on a treadmill. We obtained the average absolute error of 2.93 beats per minute and average relative error of 2.31 beats per minute, which are 121% and 119% improvement, respectively, when compared to the previously published algorithm [12].