Data-Driven Design of Self-Adhesive Epidermal Electrodes and Sensors

Stretchable and self-adhesive epidermal electrodes and sensors with long-term stability are highly desirable for wearable applications. These electrodes and sensors typically comprise multiple components, each contributing distinct mechanical and electrical functions. However, their materials design and optimization rely heavily on time-consuming trial-and-error approaches, highlighting the need for a more effective strategy. Here, we report a data-driven composition optimization strategy integrating artificial neural network (ANN) modeling and genetic algorithm (GA) optimization for the rational design of self-adhesive epidermal electrodes/sensors. By defining optimization objectives that prioritized either high electrical conductivity and adhesion or high piezoresistive sensitivity, stretchable epidermal electrodes and sensors were developed. The optimized electrode exhibits high stretchability (∼ 177%), robust adhesion (0.10 N cm^{- 1}), and low skin electrode contact impedance (∼ 72 kΩ at 10 Hz), enabling more stable long-term acquisition of electromyograms (EMG), electrocardiograms (ECG), and electroencephalograms (EEG) signals, compared to commercial Ag/AgCl gel electrodes. The resulting optimal sensor based on a different material composition demonstrates large stretchability (∼ 153%) and good piezoresistive sensitivity (gauge factor ∼ 4.79), enabling motion monitoring and human-machine interface demonstrations. This work highlights the effectiveness of data-driven optimization for application-specific design of wearable devices.