Wearable sound detectors require strain sensors that are stretchable, sensitive, and capable of adhering conformably to the skin, and toward this end, 2D materials hold great promise. However, the vibration of vocal cords and muscle contraction are complex and changeable, which can compromise the sensing performance of devices. By combining deep learning and 2D MXenes, an MXene-based sound detector is prepared successfully with improved recognition and sensitive response to pressure and vibration, which facilitate the production of a high-recognition and resolution sound detector. By training and testing the deep learning network model with large amounts of data obtained by the MXene-based sound detector, the long vowels and short vowels of human pronunciation are successfully recognized. The proposed scheme accelerates the application of artificial throat devices in biomedical fields and opens up practical applications in voice control, motion monitoring, and many other fields.
Cracks play an important role in strain sensors. However, a systematic analysis of how cracks influence the strain sensors has not been proposed. In this work, an intelligent and highly sensitive strain sensor based on indium tin oxide (ITO)/polyurethane (PU) micromesh is realized. The micromesh has good skin compatibility, water vapor permeability, and stability. Due to the color of the ITO/PU micromesh, it can be invisible on the skin. Based on the fragility of ITO, the density and resistance of cracks in the micromesh are greatly improved. Therefore, the ITO/PU micromesh strain sensor (IMSS) has an ultrahigh gauge factor (744.3). In addition, a finite element model based on four resistance layers is proposed to explain the performance of the IMSS and show the importance of high-density cracks. Compared with other strain sensors based on low-density cracks, the IMSS based on high-density cracks has larger sensitivity and better linearity. Physiological signals, such as respiration, pulse, and joint motion, can be monitored using the IMSS self-fixed on the skin. Finally, an invisible and artificial throat has been realized by combining the IMSS with a convolutional neural network algorithm. The artificial throat can translate the throat vibrations of the tester automatically with an accuracy of 86.5%. This work has great potential in health care and language function reconstruction.
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