A physicochemical-sensing electronic skin for stress response monitoring. Researchers developed an electronic skin (CARES) capable of non-invasively monitoring three vital signs (pulse waveform, galvanic skin response, and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions, and ammonium). The device uses a general approach to prepare electrochemical sensors with stable interfaces, achieving over 100 hours of continuous operation with minimal signal drift. The CARES platform includes a multi-layered sensor patch and a microfluidic module for sweat sampling, enabling 24-hour continuous monitoring of daily activities. With a machine learning pipeline, the platform can differentiate three stressors with 98.0% accuracy and quantify psychological stress responses with 98.7% confidence. The device also includes physical sensors for monitoring stress-related vital signs and a flexible polyimide substrate for robust sensing. The CARES system was tested on ten healthy subjects using three stressors (cold pressor test, virtual reality challenge, and intense exercise), demonstrating its ability to monitor stress-induced biological signals. The system's machine learning models achieved high accuracy in classifying stressors and evaluating state anxiety levels. The CARES platform offers a robust, non-invasive solution for stress response monitoring with potential applications in healthcare and human performance monitoring.A physicochemical-sensing electronic skin for stress response monitoring. Researchers developed an electronic skin (CARES) capable of non-invasively monitoring three vital signs (pulse waveform, galvanic skin response, and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions, and ammonium). The device uses a general approach to prepare electrochemical sensors with stable interfaces, achieving over 100 hours of continuous operation with minimal signal drift. The CARES platform includes a multi-layered sensor patch and a microfluidic module for sweat sampling, enabling 24-hour continuous monitoring of daily activities. With a machine learning pipeline, the platform can differentiate three stressors with 98.0% accuracy and quantify psychological stress responses with 98.7% confidence. The device also includes physical sensors for monitoring stress-related vital signs and a flexible polyimide substrate for robust sensing. The CARES system was tested on ten healthy subjects using three stressors (cold pressor test, virtual reality challenge, and intense exercise), demonstrating its ability to monitor stress-induced biological signals. The system's machine learning models achieved high accuracy in classifying stressors and evaluating state anxiety levels. The CARES platform offers a robust, non-invasive solution for stress response monitoring with potential applications in healthcare and human performance monitoring.