Avinash Alagumalai, Wan Shou, Omid Mahian, Mortaza Aghbashlo, Meisam Tabatabaei, Somchai Wongwises, Yong Liu, Antonio Torralba, Jun Chen, ZhongLin Wang, Wojciech Matusik
Self-powered sensing systems augmented with machine learning (ML) represent a path toward the large-scale deployment of the internet of things (IoT). With autonomous energy-harvesting techniques, intelligent systems can continuously generate data and process them to make informed decisions. The development of self-powered intelligent sensing systems will revolutionize the design and fabrication of sensors and pave the way for intelligent robots, digital health, and sustainable energy. However, challenges remain regarding stable power harvesting, seamless integration of ML, privacy, and ethical implications. In this review, we first present three self-powering principles for sensors and systems, including triboelectric, piezoelectric, and pyroelectric mechanisms. Then, we discuss the recent progress in applied ML techniques on self-powered sensors followed by a new paradigm of self-powered sensing systems with learning capability and their applications in different sectors. Finally, we share our outlook of potential research needs and challenges presented in ML-enabled self-powered sensing systems and conclude with a road map for future directions.