Advances in Machine Learning‐Enabled Self‐Powered Flexible Sensing Materials and Their Applications

ABSTRACT In recent years, as people's demand for quality of life and intelligence level continues to rise, the requirements for higher reliability and intelligence have become urgent. Although traditional flexible materials are widely used in the field of intelligent sensing and monitoring by virtue of their excellent flexibility, self‐adhesion and biocompatibility, they are difficult to provide long‐term stable power support in dynamic environments. Flexible materials based on self‐powered mechanisms effectively overcome this limitation by eliminating the need for an external power source and demonstrating superior stability and functionality. In addition, self‐powered mechanisms can integrate different types of sensors through multi‐mode combination, broadening the range of applications. Machine learning (ML) not only accurately and efficiently analyzes multi‐channel, multi‐modal sensor data but also facilitates the development of efficient mechanisms for evaluating self‐powered device performance (structural quality, electrical output, internal impedance, etc.). By integrating interpretable models, it provides clear guidance for material and structural optimization of self‐powered devices, thereby enhancing design efficiency. In this paper, we systematically sort out the architectures of flexible materials and self‐powered principle, construct a ML framework covering the entire data processing process, summarize the applications of ML‐assisted self‐powered flexible sensors in healthcare, pattern recognition, information security, intelligent transportation and smart cities, and provide important references and insights for ML‐enabled self‐powered flexible materials to promote their innovative applications in the field of intelligent monitoring.