Recently, there has been an increasing consumption of fossil fuels such as oil and natural gas in both industrial production and daily life. This high demand for non-renewable energy sources has prompted researchers to investigate sustainable and renewable energy alternatives. The development and production of nanogenerators provide a promising solution to address the energy crisis. Triboelectric nanogenerators, in particular, have attracted significant attention due to their portability, stability, high energy conversion efficiency, and compatibility with a wide range of materials. Triboelectric nanogenerators (TENGs) have many potential applications in various fields, such as artificial intelligence (AI) and the Internet of Things (IoT). Additionally, by virtue of their remarkable physical and chemical properties, two-dimensional (2D) materials, such as graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), MXenes, and layered double hydroxides (LDHs), have played a crucial role in the advancement of TENGs. This review summarizes recent research progress on TENGs based on 2D materials, from materials to their practical applications, and provides suggestions and prospects for future research.

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning techniques, such as the convolutional neural network, recurrent neural network, and transformer methods, provide effective solutions that can automatically extract data features and recognize patterns, significantly improving the analysis of e-skin data. Deep learning is not only capable of handling multimodal data but can also provide real-time response and personalized predictions in dynamic environments. Nevertheless, problems such as insufficient data annotation and high demand for computational resources still limit the application of e-skin. Optimizing deep learning algorithms, improving computational efficiency, and exploring hardware–algorithm co-designing will be the key to future development. This review aims to present the deep learning techniques applied in e-skin and provide inspiration for subsequent researchers. We first summarize the sources and characteristics of e-skin data and review the deep learning models applicable to e-skin data and their applications in data analysis. Additionally, we discuss the use of deep learning in e-skin, particularly in health monitoring and human–machine interactions, and we explore the current challenges and future development directions.