Fully Wearable Devices for Real-Time Health Monitoring and Multimodal Sensing in Nanomedicine Using Multiplexed Green...

Area of Science:

• Wearable technology
• Biomedical engineering
• Biosensors

Background:

• Real-time health monitoring requires stable power, on-site processing, and multimodal sensing in wearable devices.
• Existing wearable sensors face limitations in continuous power supply and comprehensive physiological data acquisition.

Purpose of the Study:

• To develop the first self-powered and fully wearable sensor (MESFW) for continuous, multimodal physiological monitoring.
• To enable sensitive detection of key biomarkers like glucose and alcohol in noninvasive biofluids.

Main Methods:

• Integration of a microfluidic module, sensing module, laser-induced graphene (LIG) electrodes, and custom electronics.
• Utilizing multiplexed green biofuels for self-powered operation.
• Employing machine learning for data analysis and health prediction.

Main Results:

• The MESFW demonstrated highly sensitive detection of glucose and alcohol in various biofluids (sweat, breath, saliva, tears).
• Continuous 24-hour power supply and real-time tracking of glucose/alcohol levels were achieved.
• Accurate identification of wearing types and real-time health prediction using machine learning.

Conclusions:

• The MESFW represents a significant advancement in self-powered wearable health monitoring.
• This technology offers a novel platform for noninvasive, real-time physiological monitoring and health prediction.
• The device overcomes power and sensing limitations of current wearable health technologies.