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Flexible sensors based on laser‐induced graphene (LIG) are widely used in wearable personal devices, with the morphology and lattice arrangement of LIG the key factors affecting their performance in various applications. In this study, femtosecond‐laser‐induced MXene‐composited graphene (LIMG) is used to improve the electrical conductivity of graphene by incorporating MXene, a 2D material with a high concentration of free electrons, into the LIG structure. By combining pump‐probe detection, laser‐induced breakdown spectroscopy (LIBS), and density functional theory (DFT) calculations, the morphogenesis and lattice structuring principles of LIMG is explored, with the results indicating that MXene materials are successfully embedded in the graphene lattice, altering both their morphology and electrical properties. The structural sparsity and electrical conductivity of LIMG composites (up to 3187 S m−1) are significantly enhanced compared to those of LIG. Based on these findings, LIMG has been used in wearable electronics. LIMG electrodes are used to detect uric acid, with a minimum detection limit of 2.48 µM. Additionally, LIMG‐based pressure and bending sensors have been successfully used to monitor human limb movement and pulse. The direct in situ femtosecond laser patterning synthesis of LIMG has significant implications for developing flexible wearable electronic sensors. [ABSTRACT FROM AUTHOR]

The continuous advancement of electronic technology has led to the gradual integration of automated intelligent devices into various aspects of human life. Motion gesture-based human–computer interaction systems offer abundant information, user-friendly functionalities, and visual cues. Surface electromyography (sEMG) signals enable the decoding of muscle movements, facilitating the realization of corresponding control functions. Considering the inherent instability and minuscule nature of sEMG signals, this thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling for the calculation of similarity between the sample and the model. This process facilitates gesture recognition and ensures effective communication between individuals and the 3D printed prosthesis. Utilizing this algorithm, the best feature model was generated by amalgamating six types of gesture classification model. A total of 600 training and evaluation experiments were performed, with each movement recognized 100 times. The experimental tests demonstrate that the accuracy of gesture recognition and prosthetic limb control using the temporal dynamic regularization algorithm achieves an impressive 93.75%, surpassing the performance of the traditional threshold control switch. [ABSTRACT FROM AUTHOR]