Electronic tattoos (e-tattoos), also known as epidermal electronics, are ultra-thin and ultra-soft noninvasive but skin-conformable devices with capabilities including physiological sensing and transdermal stimulation and therapeutics. The fabrication of e-tattoos out of conventional inorganic electronic materials used to be tedious and expensive. Recently developed cut-and-paste method has significantly simplified the process and lowered the cost. However, existing cut-and-paste method entails a medical tape on which the electronic tattoo sensors should be pasted, which increases tattoo thickness and degrades its breathability. To address this problem, here we report a slightly modified cut-and-paste method to fabricate low-cost, open-mesh e-tattoos with a total thickness of just 1.5 μm. E-tattoos of such thinness can be directly pasted on human skin and conforms to natural skin texture. We demonstrate that this ultra-thin, tape-free e-tattoo can confidently measure electrocardiogram (ECG), skin temperature, and skin hydration. Heart rate and even respiratory rate can be extracted from the ECG signals. A special advantage of such ultra-thin e-tattoo is that it is capable of high-fidelity sensing with minimized motion artifacts under various body movements. Effects of perspiration are found to be insignificant due to the breathability of such e-tattoos.

The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various disturbances. This paper develops a silent speech strategy to achieve all-weather, natural interactions. The strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily environments. In the strategy, the tattoo-like electronics imperceptibly attached on facial skin record high-quality bio-data of various silent speech, and the machine-learning algorithm deployed on the cloud recognizes accurately the silent speech and reduces the weight of the wireless acquisition module. A series of experiments show that the silent speech recognition system (SSRS) can enduringly comply with large deformation (~45%) of faces by virtue of the electricity-preferred tattoo-like electrodes and recognize up to 110 words covering daily vocabularies with a high average accuracy of 92.64% simply by use of small-sample machine learning. We successfully apply the SSRS to 1-day routine life, including daily greeting, running, dining, manipulating industrial robots in deafening noise, and expressing in darkness, which shows great promotion in real-world applications.

The facial expressions are a mirror of the elusive emotion hidden in the mind, and thus, capturing expressions is a crucial way of merging the inward world and virtual world. However, typical facial expression recognition (FER) systems are restricted by environments where faces must be clearly seen for computer vision, or rigid devices that are not suitable for the time-dynamic, curvilinear faces. Here, we present a robust, highly wearable FER system that is based on deep-learning-assisted, soft epidermal electronics. The epidermal electronics that can fully conform on faces enable high-fidelity biosignal acquisition without hindering spontaneous facial expressions, releasing the constraint of movement, space, and light. The deep learning method can significantly enhance the recognition accuracy of facial expression types and intensities based on a small sample. The proposed wearable FER system is superior for wide applicability and high accuracy. The FER system is suitable for the individual and shows essential robustness to different light, occlusion, and various face poses. It is totally different from but complementary to the computer vision technology that is merely suitable for simultaneous FER of multiple individuals in a specific place. This wearable FER system is successfully applied to human-avatar emotion interaction and verbal communication disambiguation in a real-life environment, enabling promising human-computer interaction applications.