Wearable Wrist Photoplethysmography for Optimal Monitoring of Vital Signs: A Unified Perspective on Pulse Waveforms

Abstract

To track vital signs on the wrist, a wearable system, named WrisTee, has been developed for remote health monitoring. Using three optical sensors with four light sources, WrisTee allows users to measure 12 photoplethysmogram (PPG) signals at three locations on the radial artery. Two types of PPG signals with opposite polarities were discovered and designated as in-phase and invert-phase signals. We provided a unified viewpoint regarding their differences based on the Beer $-$ Lambert law, and showed that both signals can be used for heart monitoring using data analyzed from a selected subject. Using reflective pulse-transition time (R-PTT) and the standard deviation of R-PTT ($\sigma _{R\text{-}PTT}$), we proposed a method for selecting the optimal wavelength to achieve the best quality signal, thus minimizing storage requirements, power resources, and computational costs. We conducted an experiment on ten subjects to evaluate the feasibility of the proposed method. Our results demonstrated that WrisTee is capable of finding the optimal positions and wavelengths for monitoring vital signs. To automatically detect the PPG phases, six machine learning (ML) models were explored to assess their accuracy for PPG-phase classification. The experimental results show that a convolutional neural network can be the best candidate for phase classification. Hence, it can be integrated into WrisTee for noninvasive health monitoring such as heart rate, heart rate variability, or blood pressure. Our work paves a new direction in bio-signal medical researches by adopting in-phase and invert-phase PPGs for healthcare monitoring.

Keywords

• Blood pressure
• in-phase
• invert-phase
• machine learning
• multi-wavelength
• photoplethysmogrpahy