Liu Guodong, Wang Heng, Zhang Pingna, Liu Yuxuan, Tan Qifeng, Jin Xin, Li Chaojiang
School of Mechanical Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China.
Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314011, China.
Mikrochim Acta. 2025 Aug 19;192(9):604. doi: 10.1007/s00604-025-07466-6.
This study presents a wearable multi-parameter electrochemical detection system (WMEDS) designed for the simultaneous monitoring of sweat biomarkers. The system integrates a microcontroller, a dual-mode signal acquisition circuit, and a low-power Bluetooth module. To address the non-stationary electrochemical signals, a preprocessing approach combining Savitzky-Golay filtering with Z-score-difference detection is employed. The core algorithm incorporates wavelet hierarchical threshold denoising, which improves the average signal-to-noise ratio by 12 dB. This signal processing method effectively suppresses noise while preserving key signal features (peak retention rate of 97.3%), resulting in a smoothness index exceeding 0.99, and the root mean square remains below ± 0.5%. The efficacy of the WMEDS is demonstrated in Ca and glucose detection, indicating comparable results with those obtained from commercial electrochemical workstations. Furthermore, the WMEDS achieves highly accurate detection of Ca (sensitivity of 30.6 mV/decade) and glucose (sensitivity of 7.7 μA/mM), with quantitative errors less than 5.5% and on-body test fluctuations below 2.2%, thereby fulfilling the performance requirements for wearable sweat monitoring applications.
本研究提出了一种用于同时监测汗液生物标志物的可穿戴多参数电化学检测系统（WMEDS）。该系统集成了一个微控制器、一个双模信号采集电路和一个低功耗蓝牙模块。为处理非平稳电化学信号，采用了将Savitzky-Golay滤波与Z分数差分检测相结合的预处理方法。核心算法包含小波分层阈值去噪，可将平均信噪比提高12 dB。这种信号处理方法在保留关键信号特征（峰值保留率为97.3%）的同时有效抑制噪声，得到的平滑度指数超过0.99，均方根保持在±0.5%以下。WMEDS在钙和葡萄糖检测中展现出有效性，结果与商用电化学工作站相当。此外，WMEDS对钙（灵敏度为30.6 mV/十倍浓度变化）和葡萄糖（灵敏度为7.7 μA/mM）实现了高精度检测，定量误差小于5.5%，体表测试波动低于2.2%，从而满足了可穿戴汗液监测应用的性能要求。
