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Abstract Nanomaterial‐based stretchable electronics composed of conductive nanomaterials in elastomer can seamlessly integrate with human skin to imperceptibly capture electrophysiological signals. Despite the use of transfer printing to form embedded structures, it remains challenging to facilely and stably integrate conductive nanomaterials with thin, low‐modulus, adhesive elastomers. Here, a facile‐yet‐simple laser‐induced graphene (LIG)‐assisted patterning and transfer method is demonstrated to integrate patterned silver nanowires onto an ultra‐low modulus silicone adhesive as ultra‐conformal epidermal electrodes. The resulting thin epidermal electrodes of ≈50 µm exhibit a low sheet resistance (0.781 Ω sq⁻¹), tissue‐like Young's modulus (0.53 MPa), strong self‐adhesion, and excellent breathability. The breathable electrodes dynamically conformed to the skin with low contact impedance allow for long‐term, high‐fidelity monitoring of electrophysiological signals in complex environments (even during exercise and heavy sweating). Moreover, the LIG‐assisted transfer can provide a robust interface to establish a stable connection between the soft electrodes and rigid hardware. The large‐scale fabrication further provides an eight‐channel electromyography system combined with a deep learning algorithm for gesture classification and recognition with remarkable accuracy (95.4%). The results from this study also provide design guidelines and fabrication methods of the next‐generation epidermal electronics for long‐term dynamic health monitoring, prosthetic control, and human‐robot collaborations. 

Abstract Skin‐interfaced high‐sensitive biosensing systems to detect electrophysiological and biochemical signals have shown great potential in personal health monitoring and disease management. However, the integration of 3D porous nanostructures for improved sensitivity and various functional composites for signal transduction/processing/transmission often relies on different materials and complex fabrication processes, leading to weak interfaces prone to failure upon fatigue or mechanical deformations. The integrated system also needs additional adhesive to strongly conform to the human skin, which can also cause irritation, alignment issues, and motion artifacts. This work introduces a skin‐attachable, reprogrammable, multifunctional, adhesive device patch fabricated by simple and low‐cost laser scribing of an adhesive composite with polyimide powders and amine‐based ethoxylated polyethylenimine dispersed in the silicone elastomer. The obtained laser‐induced graphene in the adhesive composite can be further selectively functionalized with conductive nanomaterials or enzymes for enhanced electrical conductivity or selective sensing of various sweat biomarkers. The possible combination of the sensors for real‐time biofluid analysis and electrophysiological signal monitoring with RF energy harvesting and communication promises a standalone stretchable adhesive device platform based on the same material system and fabrication process. 

Abstract General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants’ general movements can be captured digitally, but the lack of quantitative assessment and well‐trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low‐resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy‐to‐use features. This work presents a sparse sensor network with soft wireless IMU devices (SWDs) for automatic early evaluation of general movements in infants. The sparse network consisting of only five sensor nodes (SWDs) with robust mechanical properties and excellent biocompatibility continuously and stably captures full‐body motion data. The proof‐of‐the‐concept clinical testing with 23 infants showcases outstanding performance in recognizing neonatal activities, confirming the reliability of the system. Taken together with a tiny machine learning algorithm, the system can automatically identify risky infants based on the GMs, with an accuracy of up to 100% (99.9%). The wearable sparse sensor network with an artificial intelligence‐based algorithm facilitates intelligent evaluation of infant brain development and early diagnosis of development disorders.