Epidermal electrophysiology is widely carried out for disease diagnosis, performance monitoring, human-machine interaction, etc. Compared with thick, stiff, and irritating gel electrodes, emerging tattoo-like epidermal electrodes offer much better wearability and versatility. However, state-of-the-art tattoo-like electrodes are limited in size (e.g., centimeters) to perform electrophysiology at scale due to challenges including large-area fabrication, skin lamination, and electrical interference from long interconnects. Therefore, we report large-area, soft, breathable, substrate- and encapsulation-free electrodes designed into transformable filamentary serpentines that can be rapidly fabricated by cut-and-paste method. We propose a Cartan curve–inspired transfer process to minimize strain in the electrodes when laminated on nondevelopable skin surfaces. Unwanted signals picked up by the unencapsulated interconnects can be eliminated through a previously unexplored electrical compensation strategy. These tattoo-like electrodes can comfortably cover the whole chest, forearm, or neck for applications such as multichannel electrocardiography, sign language recognition, prosthetic control or mapping of neck activities.

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.

Human-machine interfaces (HMIs) are widely studied to understand the human biomechanics and/or physiology and the interaction between humans and machines/robots. The conventional rigid or invasive HMIs that record/send information from/to human bodies have significant disadvantages in practice for longterm, portable, and comfortable usages. To better adapt to natural soft skins, soft HMIs have been designed to deform into arbitrary shapes, and their bendable, stretchable, compressible and twistable properties offer a huge potential in future personalized applications. This paper presents a survey on various soft HMIs in terms of design, sensing, stimulation as well as their applications. Specifically, tactile / motion / bio-potential sensors are categorized for recording various data from human bodies, while stimulators are discussed for information feedback and motion activation to human bodies. It is anticipated that soft HMIs will promote the interaction among humans, machines/robots and environment to achieve desired coexisting-cooperativecognitive function in a robot system, named as Tri-Co Robot, for the human-centered applications, such as rehabilitation, medical monitoring and human-robot cooperation.

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.

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Advances in fabric strain sensors have established a route to comfortableto-wear flexible electronics with particularly remarkable permeability and low modulus due to their porous fabric microstructure. A key challenge that remains unsolved is to regulate the sensor response via on-demand design for a variety of application scenarios to sufficiently exploit the highest possible sensitivity. While recent reports have described a variety of options in varying the material and orientation of the overall fiber mat, the development of approaches where multiple sensors with different responses can be integrated on a single substrate without affecting macroscopic mechanical properties remains an area of continued interest. Herein, a simple mechanical strategy is reported, which plates the patterned functional material on the fabric mat at a pre-stretched state in the prescribed direction, and control of direction and prestrain forms either sensors with different responses or strain-insensitive interconnects. A systematic study has revealed the underlying mechanism of this strategy, which can serve as a guideline for the on-demand design and fabrication of fabric strain sensors. Demonstration applications in motion monitoring bandages and gesture recognition gloves illustrate capabilities in functional epidermal sensing devices.

Six chest leads are the standardized clinical devices of diagnosing cardiac diseases. Emerging epidermal electronics technology shift the dangling wires and bulky devices to imperceptible wearing, achieving both comfortable experience and high-fidelity measuring. Extending small areas of current epidermal electronics to the chest scale requires eliminating interference from long epidermal interconnects and rendering the data acquisition (DAQ) portable. Herein, we developed a chest-scale epidermal electronic system (EES) for standard precordial-lead ECG and hydration monitoring, including the only μm-thick substrate-free epidermal sensing module and the soft wireless DAQ module. An electrical compensation strategy using double channels within the DAQ module and epidermal compensated branches (ECB) is proposed to eliminate unwanted signals from the long epidermal interconnects and to achieve the desired ECG. In this way, the EES works stably and precisely under different levels of exercise. Patients with sinus arrhythmias have been tested, demonstrating the prospect of EES in cardiac diseases.