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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 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.