Wearable Multichannel Photoplethysmography Framework for Heart Rate Monitoring During Intensive Exercise

Abstract

We propose a multichannel-PPG sensor that uses truncated singular value decomposition (SVD) for accurate estimation of heart rate (HR) during high-intensity exercise. We implemented a sensor to acquire nine channel signals simultaneously, and to enable real-time monitoring of PPG signals and HR. For denoising, we used SVD programmed in the microcontroller unit and showed that the pure pulse is concentrated mostly within the first largest singular value with the related two eigenvectors by extracting the significant feature components of the pulse signals. Our proposed sensor was tested in 32 subjects during exercise of various intensities: walking and medium- and high-intensity jogging. We compared our proposed method with the single-channel method, the multichannel weighting method, and the multichannel best signal selection. Furthermore, we incorporated acceleration signals and a HR tracking algorithm, into our proposed multichannel framework and evaluated its performance using the 12 data sets from the 2015 IEEE Signal Processing Cup Database. Our method showed the lowest absolute errors in HR estimation when compared to the single-channel, multichannel weighting, and multichannel best signal selection methods, irrespective of exercise intensity. We also found that our proposed multichannel framework incorporating acceleration signals and a HR tracking algorithm provided the lowest average absolute error of 0.94 beats/m. The results show that our proposed multichannel framework can be used for accurate HR estimation during high-intensity exercise.