Reliability of flexible pressure sensors under sweat, moisture, and environmental stress: Mechanisms, materials, packaging, and data-driven mitigation

The development of wearable technology and electronic skin (e-skin) depends on flexible pressure sensors because they convert mechanical signals into electrical outputs. The devices which respond quickly and have comfortable designs for skin contact do not resolve the issue of maintaining device reliability during extended use in real-world body-worn applications. This review centers sweat- and moisture-dominated failure pathways that govern stability during extended wear. In addition, examines how occlusive patches create a harsh skin microenvironment during continuous wear. Moreover, examines how these conditions affect different fields which include sports, fitness applications, medical bandaging practices, virtual reality and industrial wearable technology. Notably, we then map moisture-activated degradation mechanisms across key material families, including polymer swelling and plasticization, leaching and ionic migration, interfacial delamination, biofouling, corrosion of silver-based conductors via chloride chemistry, humidity-driven conductivity drift in PEDOT: PSS, drying/swelling instabilities in hydrogels/ionogels, and the water/oxygen oxidation vulnerability of MXenes. The research evaluates different device-level reliability engineering approaches which combine three encapsulation techniques (hermetic and breathable and hybrid) with four structural designs (porous/foam and microfluidic for sweat control and multilayer wettability-gradient stacks and wet-skin adhesion solutions) to prevent edge lift-off and delamination. However, the study uses compiled literature records to demonstrate that there are no established reliability endpoints for retention and drift and hysteresis tests and that essential cycling and environmental test conditions remain unreported. Consequently, the paper concludes by presenting data-based methods which use machine learning to enhance system reliability through drift correction, anomaly identification, multi-objective design optimization and physics-based modeling systems. The research identifies three main areas which need additional work regarding available data collections and performance assessment standards and official industry standards.