Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor

TLDR

Wearable bioelectronics capable of continuous pulse‑wave monitoring that resists motion and sweat remain a major challenge and highly desired. The study develops a low‑cost, lightweight textile triboelectric sensor that converts arterial pulsatility‑induced skin deformation into electricity for high‑fidelity, continuous pulse‑wave monitoring in ambulatory, sweaty settings. The sensor achieves a 23.3‑dB SNR, 40‑ms response, and 0.21 µA kPa⁻¹ sensitivity, and, combined with machine‑learning algorithms, it precisely measures systolic and diastolic pressure validated against a cuff, while a custom app enables one‑click data sharing and diagnosis. The resulting wireless biomonitoring system offers a practical paradigm for continuous, personalized cardiovascular characterization in the IoT era.

Abstract

Abstract 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.