Triboelectric Nanogenerators with Machine Learning for Internet of Things

Abstract

The development of the Internet of Things (IoT) indicates that humankind has entered a new intelligent era of the “Internet of Everything”. Thanks to the characteristics of low-cost, diverse structure, and high energy conversion efficiency, the self-powered sensing systems, which are based on the Triboelectric Nanogenerator (TENG), demonstrate great potential in the field of IoT. In order to solve the challenges of TENG in sensing signal processing, such as signal noise and nonlinear relations, Machine Learning (ML), which is an efficient and mature data processing tool, is widely applied for efficiently processing the large and complex output signal data generated by TENG intelligent sensing system. This review summarizes and analyzes the adaptation of different algorithms in TENG and their advantages and disadvantages at the beginning, which provides a reference for the selection of algorithms for TENG. More importantly, the application of TENG is introduced in multiple scenarios, including health monitoring, fault detection, and human-computer interaction. Finally, the limitations and development trend of the integration of TENG and ML are proposed by classification to promote the future development of the intelligent IoT era.