Personalized Machine Learning-Coupled Nanopillar Triboelectric Pulse Sensor for Cuffless Blood Pressure Continuous Monitoring

A wearable system that can continuously track the fluctuation of blood pressure (BP) based on pulse signals is highly desirable for the treatments of cardiovascular diseases, yet the sensitivity, reliability, and accuracy remain challenging. Since the correlations of pulse waveforms to BP are highly individualized due to the diversity of the patients' physiological characteristics, wearable sensors based on universal designs and algorithms often fail to derive BP accurately when applied on individual patients. Herein, a wearable triboelectric pulse sensor based on a biomimetic nanopillar layer was developed and coupled with Personalized Machine Learning (ML) to provide accurate and continuous monitoring of BP. Flexible conductive nanopillars as the triboelectric layer were fabricated through soft lithography replication of a cicada wing, which could effectively enhance the sensor's output performance to detect weak signal characteristics of pulse waveform for BP derivation. The sensors were coupled with a personalized Partial Least-Squares Regression (PLSR) ML to derive unknown BP based on individual pulse characteristics with reasonable accuracy, avoiding the issue of individual variability that was encountered by General PLSR ML or formula algorithms. The cuffless and intelligent design endow this ML-sensor as a highly promising platform for the care and treatments of hypertensive patients.