Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.

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 ho

SUBMITTER: Fang Y 

PROVIDER: S-EPMC9205313 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature