Advances in Machine Learning-Driven Flexible Strain Sensors: Challenges, Innovations, and Applications

Flexible strain sensors have garnered significant attention due to their high sensitivity, rapid response, and flexibility. Recent innovations, particularly those incorporating machine learning, have significantly enhanced their stability, sensitivity, and adaptability, positioning these sensors as promising solutions in health monitoring, human-computer interaction, and smart home applications. However, challenges remain in optimizing sensor materials for enhanced responsiveness, durability, and stability. Moreover, the development of machine learning-based strain sensors faces obstacles, including algorithmic limitations, low noise tolerance in complex environments, and limited model interpretability. This review systematically evaluates the latest advancements in flexible strain sensors, emphasizing the critical role of machine learning in performance enhancement. It further explores the shift from traditional machine learning methods to deep learning approaches, elucidating the potential applications that these algorithms facilitate. Finally, we discuss future research trajectories, highlighting both opportunities and challenges that may guide the next wave of innovations in this dynamic field.