Flexible Multimodal Magnetoresistive Sensors Based on Alginate/Poly(vinyl alcohol) Foam with Stimulus Discriminability for Soft Electronics Using Machine Learning

Flexible foam-based sensors have attracted substantial interest due to their high specific surface area, light weight, superior deformability, and ease of manufacture. However, it is still a challenge to integrate multimodal stimuli-responsiveness, high sensitivity, reliable stability, and good biocompatibility into a single foam sensor. To achieve this, a magnetoresistive foam sensor was fabricated by an in situ freezing–polymerization strategy based on the interpenetrating networks of sodium alginate, poly(vinyl alcohol) in conjunction with glycerol, and physical reinforcement of core–shell bidisperse magnetic particles. The assembled sensor exhibited preferable magnetic/strain-sensing capability (GF ≈ 0.41 T–1 for magnetic field, 4.305 for tension, −0.735 for bending, and −1.345 for pressing), quick response time, and reliable durability up to 6000 cycles under external stimuli. Importantly, a machine learning algorithm was developed to identify the encryption information, enabling high recognition accuracies of 99.22% and 99.34%. Moreover, they could be employed as health systems to detect human physiological motion and integrated as smart sensor arrays to perceive external pressure/magnetic field distributions. This work provides a simple and ecofriendly strategy to fabricate biocompatible foam-based multimodal sensors with potential applications in next-generation soft electronics.