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The flexible strain sensors based on the textile substrate have natural flexibility, high sensitivity, and wide-range tensile response. However, the textile’s complex and anisotropic substructure leads to a negative differential resistance (NDR) response, lacking a deeper understanding of the mechanism. Therefore, we examined a graphene textile strain sensor with a conspicuous NDR tensile response, providing a requisite research platform for mechanism investigation. The pioneering measurement of single fiber bundles confirmed the existence of the NDR effect on the subgeometry scale. Based on the in situ characterization of tensile morphology and measurement, we conducted quantitative behavior analyses to reveal the origin of tensile electrical responses in the full range comprehensively. The results showed that the dominant factor in generating the NDR effect is the relative displacement of fibers within the textile bundles. Based on the neural spiking-like tensile response, we further demonstrated the application potential of the textile strain sensor in threshold detection and near-sensor signal processing. The proposed NDR behavior model would provide a reference for the design and application of wearable intelligent textiles.

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors.

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Benefiting from outstanding electrical properties and excellent flexibility, graphene is widely used in novel sensors. However, the high price, complex manufacturing process, and especially the lack of algorithm analysis for graphene signals limit the development of graphene sensors. In order to overcome these problems, this paper demonstrates a high-performance solution for audio recognition based on a novel low-cost graphene flexible microphone. The graphene microphone is fabricated by laser-induced graphene which has a lower price and a simple process compared to traditional chemical methods. Not only the production of graphene microphones, this paper also innovatively uses deep learning to achieve speech command recognition on graphene microphones. The signal characteristics of the graphene flexible microphone are analyzed, and we propose a deep learning algorithm suitable for graphene microphones. One-dimensional convolutional neural network enables the high-performance audio recognition on graphene microphones which has huge advantages over traditional pattern recognition methods. The voices of 10 numbers and 20 sentences were collected 50 times to build a data set with a total of 1500 samples. For 10 numbers from 0 to 9, the deep learning model achieved an average correct rate of 98% which is far more than 84.5% of the traditional method. Finally, 20 sentences were used to test the performance of this solution under more vocabulary and the accuracy rate was 98.25%. With the help of deep learning, this solution already has the basic functions of a simple speech recognition system. This paper shows a complete application that brings graphene microphones from theoretical research to practical applications and provides reference for signal analysis and algorithm research of more novel sensors.