On the design and implementation of a highly accurate pulse predictor for exercise equipment
Date of Original Version
Goal: This study aims to develop highly accurate heart rate monitoring from the hand-held contact signal within a noisy environment during exercise. Methods: 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. Results: 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. Conclusion: HAPPEE is able to accurately monitor heart rate within a noisy environment during exercise. Significance: 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.
IEEE Transactions on Biomedical Engineering
Kay, Steven, Quan Ding, and Dongyang Li. "On the design and implementation of a highly accurate pulse predictor for exercise equipment." IEEE Transactions on Biomedical Engineering 62, 8 (2015): 1918-1926. doi:10.1109/TBME.2015.2407155.