A wireless fully-passive acquisition of biopotentials.

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Sensor Technology

Background:

• Biopotential signals are crucial for organ function assessment and disease diagnosis.
• Current methods often rely on wired systems, limiting patient mobility and comfort.
• There is a need for unobtrusive, real-time biopotential monitoring solutions.

Purpose of the Study:

• To develop and validate a flexible, wireless, and fully-passive sensor for real-time biopotential acquisition.
• To evaluate the performance of the wireless sensor against traditional wired systems.
• To demonstrate the feasibility of the sensor for various biopotential measurements, including ECG, EMG, and EOG.

Main Methods:

• A flexible sensor fabricated on a polyimide substrate.
• Utilized Radio Frequency (RF) microwave backscattering for wireless data transmission.
• Employed varactors to modulate incoming RF signals with biopotentials.
• Validated with emulated signals, Electrocardiogram (ECG), Electromyogram (EMG), and Electrooculogram (EOG).
• Applied deep learning algorithms for signal quality analysis and performance comparison.

Main Results:

• The wireless sensor demonstrated <3% discrepancy in deep learning accuracy for ECG and EMG compared to wired sensors up to 240 mm.
• Achieved high deep learning accuracy (93.6% training, 92.2% testing) for wireless Electrooculogram (EOG) acquisition.
• Successfully detected biopotential signals as low as 250 μV_PP.
• Confirmed real-time, wireless, and fully-passive biopotential acquisition is feasible.

Conclusions:

• The developed flexible sensor offers a viable solution for real-time, wireless, and fully-passive biopotential monitoring.
• The system shows high accuracy and reliability, comparable to existing wired technologies.
• This technology holds significant potential for advancing clinical research and remote patient monitoring.