Monitoring blood pressure and cardiac function without positioning via a deep learning–assisted strain sensor array
Shuo Li, Haomin Wang, Wei Ma, Lin Qiu, Kailun Xia, Yong Zhang, Yong Zhang, Haojie Lü, Mengjia Zhu, Xiaoping Liang, Xun‐En Wu, Huarun Liang, Yingying Zhang, Yingying Zhang
Abstract
Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.