Intelligent Biomedical Photoplethysmography Signal Cycle Division With Digital Twin in Metaverse for Consumer Health
Dazhou Li, X. Chen, Qing Li, Fa Zhu, Xiaoyang Lu, Sidheswar Routray, Uttam Ghosh, Mohammed S. Al-Numay
Abstract
The integration of digital twin technology and the Metaverse has attracted widespread attention in the field of consumer health. Meanwhile, the immersive virtual world, Metaverse, has been increasingly used as a series of consumer health platforms. Photoplethysmography (PPG), as a convenient and low-cost digital twin interaction technology, has become one of the important physiological interaction technologies in the field of consumer health in the Metaverse. In this paper, we propose a PPG signal period detection (PSCD) algorithm based on the concavity of rising branches in PPG signals. By backward differentiation, the pattern of the rate of change of PPG amplitude is captured. Then, the time domain energy distribution is obtained through envelope extraction. The PSCD algorithm can identify all major valleys in the PPG signal based on long tail characteristics. Each cycle of the PPG signal is correctly divided due to changes in the envelope and physiological representation of arterial volume caused by left ventricular ejection from the ascending branch. Finally, the experimental results show that the accuracy of the proposed PSCD algorithm is higher than the other four classic algorithms.