Recent Advances in Decoupling Strategies for Soft Sensors

Soft sensors have shown great promise in emerging fields such as wearable electronics, soft robotics, personalized healthcare, and human-machine interaction. However, their practical deployment remains limited due to signal interference and cross-sensitivity arising from simultaneous mechanical and environmental stimuli. To address these challenges, this review presents an overview of recent signal decoupling strategies for accurate and stable sensing. First, approaches for suppressing interference from individual physical parameters, such as stretching, bending, temperature, humidity, pressure, and light, are discussed. Next, array-level crosstalk elimination techniques are reviewed to ensure reliable spatial resolution. Furthermore, advanced strategies based on spatiotemporal separation and machine learning are summarized for decoupling complex and coupled input signals. These approaches offer promising routes to simplify sensor design, reduce system complexity, and enhance signal fidelity. Finally, remaining challenges and future directions are discussed to guide the development of high-performance, scalable, and integrable sensing systems.