On the Design and Implementation of a Highly Accurate Pulse Predictor for Exercise Equipment.

Abstract

This study aims to develop highly accurate heart rate monitoring from the hand-held contact signal within a noisy environment during exercise. The periodic pattern and uncertainties of a physiological signal are modeled by a Laplacian random process. Based on this statistical model, a highly accurate pulse predictor (HAPPEE) is derived and implemented in real-time on a Cypress PSoC 5LP development board. A real-time experiment is designed to compare HAPPEE with a commercial heart rate monitor from POLAR. The percentage of credible estimates and the mean square error (MSE) of credible estimates are reported for experiments with seven healthy subjects. The overall percentage of credible estimates is 99.2% for HAPPEE and 93.6% for POLAR. The overall MSE of credible estimates is 3.1 for HAPPEE and 7.7 for POLAR. These results show that HAPPEE is more accurate than POLAR. HAPPEE is able to accurately monitor heart rate within a noisy environment during exercise. Unlike existing heart rate estimation methods, HAPPEE does not require pulse detection or tuning parameters. It can be easily implemented in real-time on a low power and low cost development board for exercise equipment and outperforms a commercial heart rate monitor.