Publication | Closed Access
A Wearable and Highly Sensitive Graphene Strain Sensor for Precise Home-Based Pulse Wave Monitoring
316
Citations
36
References
2017
Year
Graphene NanomeshesWearable SystemMedical MonitoringCurrent Pulse SensorsEngineeringImplantable SensorMechanical EngineeringPhysiologyWearable TechnologyBioelectronicsGrapheneProfuse Medical InformationWearable SensorsWearable ElectronicsElectrophysiologyBiomedical EngineeringHuman SkinWearable Sensor
Pulse waveforms can provide extensive cardiovascular information, yet most existing sensors are bulky or lack sufficient sensitivity. The study aims to develop a noninvasive, real‑time pulse monitoring device that is easy to use and comfortable to wear. The sensor is a graphene‑based, skin‑like patch whose substrate stiffness and interfacial bonding are tuned to balance linearity and sensitivity, enabling beat‑to‑beat radial arterial pulse measurement. Compared with bulky clinical instruments, the soft graphene patch accurately and objectively detects subtle, beat‑to‑beat pulse variations in real time—across age groups and before/after exercise—making it a promising home‑based monitoring solution.
Profuse medical information about cardiovascular properties can be gathered from pulse waveforms. Therefore, it is desirable to design a smart pulse monitoring device to achieve noninvasive and real-time acquisition of cardiovascular parameters. The majority of current pulse sensors are usually bulky or insufficient in sensitivity. In this work, a graphene-based skin-like sensor is explored for pulse wave sensing with features of easy use and wearing comfort. Moreover, the adjustment of the substrate stiffness and interfacial bonding accomplish the optimal balance between sensor linearity and signal sensitivity, as well as measurement of the beat-to-beat radial arterial pulse. Compared with the existing bulky and nonportable clinical instruments, this highly sensitive and soft sensing patch not only provides primary sensor interface to human skin, but also can objectively and accurately detect the subtle pulse signal variations in a real-time fashion, such as pulse waveforms with different ages, pre- and post-exercise, thus presenting a promising solution to home-based pulse monitoring.
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