Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor - Fang, Yunsheng; Zou, Yongjiu; Xu, Jing; Chen, Guorui; Zhou, Yihao; Deng, Weili; Zhao, Xun; Roustaei, Mehrdad; Hsiai, Tzung K.; Chen, Jun (Advanced materials (Weinheim), 2021)
Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low‐cost, lightweight, and mechanically durable textile triboelectric sensor that can convert subtle skin deformation caused by arterial pulsatility into electricity for high‐fidelity and continuous pulse waveform monitoring in an ambulatory and sweaty setting is developed. The sensor holds a signal‐to‐noise ratio of 23.3 dB, a response time of 40 ms, and a sensitivity of 0.21 µA kPa−1. With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital. Additionally, a customized cellphone application (APP) based on built‐in algorithm is developed for one‐click health data sharing and data‐driven cardiovascular diagnosis. The textile triboelectric sensor enabled wireless biomonitoring system is expected to offer a practical paradigm for continuous and personalized cardiovascular system characterization in the era of the Internet of Things. A waterproof textile‐based wearable cardiovascular monitoring system is developed via systematic integration of a triboelectric sensor, a signal processing circuit, a Bluetooth module, and a customized user‐friendly app interface. With the assistance of machine‐learning algorithms, this system can perform continuous blood pressure monitoring against body motion and perspiration.