<p>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 Ca²⁺ and glucose detection, indicating comparable results with those obtained from commercial electrochemical workstations. Furthermore, the WMEDS achieves highly accurate detection of Ca²⁺ (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.</p> Graphical Abstract <p></p>