Electronic skin (E-skin) is a crucial seamless human-machine interface (HMI), holding promise in healthcare monitoring and personal electronics. Liquid metal (LM) has been recognized as an ideal electrode material to fabricate E-skins. However, conventional sealed LM electrodes cannot expose the LM layer for direct contact with the skin resulting in the low performance of electrophysiological monitoring. Furthermore, traditional printed LM electrodes are difficult to transfer or recycle, and fractures easily occur under stretching of the substrate. Here, we report a kind of LM electrode that we call a kirigami-structured LM paper (KLP), which is self-supporting, conductor-exposing, stretchable, ultrathin, and recyclable for multifunctional E-skin. The KLP is fabricated by the kirigami paper cutting art with three types of structures including uniaxial, biaxial, and square spiral. The KLP can act as an E-skin to acquire high-quality electrophysiological signals, such as electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG). Upon integration with a triboelectric nanogenerator (TENG), the KLP can also operate as a self-powered E-skin. On the basis of the self-powered E-skin, we further developed a smart dialing communication system, which is applied on human skin to call a cellphone. Compared with conventional sealed or printed LM electrodes, the KLP can simultaneously achieve self-supporting, conductor-exposing, stretchable, ultrathin, and recyclable features. Such KLP offers potential for E-skins in healthcare monitoring and intelligent control, as well as smart robots, virtual reality, on-skin personal electronics, etc.

Human‐machine interfaces (HMIs) play important role in the communication between humans and robots. Touchless HMIs with high hand dexterity and hygiene hold great promise in medical applications, especially during the pandemic of coronavirus disease 2019 (COVID‐19) to reduce the spread of virus. However, current touchless HMIs are mainly restricted by limited types of gesture recognition, the requirement of wearing accessories, complex sensing platforms, light conditions, and low recognition accuracy, obstructing their practical applications. Here, an intelligent noncontact gesture‐recognition system is presented through the integration of a triboelectric touchless sensor (TTS) and deep learning technology. Combined with a deep‐learning‐based multilayer perceptron neural network, the TTS can recognize 16 different types of gestures with a high average accuracy of 96.5%. The intelligent noncontact gesture‐recognition system is further applied to control a robot for collecting throat swabs in a noncontact mode. Compared with present touchless HMIs, the proposed system can recognize diverse complex gestures by utilizing charges naturally carried on human fingers without the need of wearing accessories, complicated device structures, adequate light conditions, and achieves high recognition accuracy. This system could provide exciting opportunities to develop a new generation of touchless medical equipment, as well as touchless public facilities, smart robots, virtual reality, metaverse, etc.