A wearable multi-parameter electrochemical detection and signal processing system for real-time sweat analysis.

Area of Science:

• Biomedical Engineering
• Electrochemical Sensing
• Wearable Technology

Background:

• Simultaneous monitoring of sweat biomarkers is crucial for non-invasive health diagnostics.
• Existing wearable systems face challenges with signal noise and accuracy.

Purpose of the Study:

• To develop and validate a wearable multi-parameter electrochemical detection system (WMEDS) for real-time sweat analysis.
• To implement advanced signal processing techniques for improved data quality.

Main Methods:

• The WMEDS integrates a microcontroller, dual-mode signal acquisition, and Bluetooth connectivity.
• Signal preprocessing involves Savitzky-Golay filtering and Z-score-difference detection.
• Wavelet hierarchical threshold denoising is employed for noise reduction, improving signal-to-noise ratio by 12 dB.

Main Results:

• The signal processing achieved a peak retention rate of 97.3% and a smoothness index >0.99.
• The WMEDS demonstrated comparable results to commercial electrochemical workstations for Ca ²⁺ and glucose detection.
• High accuracy was achieved for Ca ²⁺ (sensitivity 30.6 mV/decade) and glucose (sensitivity 7.7 μA/mM) with quantitative errors <5.5%.

Conclusions:

• The developed WMEDS effectively monitors sweat biomarkers with high accuracy and reliability.
• The advanced signal processing significantly enhances the performance of wearable electrochemical sensors.
• The system meets the stringent performance requirements for practical wearable sweat monitoring applications.