State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
ACS Appl Mater Interfaces. 2023 Mar 8;15(9):12551-12559. doi: 10.1021/acsami.2c22287. Epub 2023 Feb 21.
Intelligent sensors have attracted substantial attention for various applications, including wearable electronics, artificial intelligence, healthcare monitoring, and human-machine interactions. However, there still remains a critical challenge in developing a multifunctional sensing system for complex signal detection and analysis in practical applications. Here, we develop a machine learning-combined flexible sensor for real-time tactile sensing and voice recognition through laser-induced graphitization. The intelligent sensor with a triboelectric layer can convert local pressure to an electrical signal through a contact electrification effect without external bias, which has a characteristic response behavior when exposed to various mechanical stimuli. With the special patterning design, a smart human-machine interaction controlling system composed of a digital arrayed touch panel is constructed to control electronic devices. Based on machine learning, the real-time monitoring and recognition of the changes of voice are achieved with high accuracy. The machine learning-empowered flexible sensor provides a promising platform for the development of flexible tactile sensing, real-time health detection, human-machine interaction, and intelligent wearable devices.
智能传感器在各种应用中引起了广泛关注,包括可穿戴电子、人工智能、医疗保健监测和人机交互。然而,在开发用于复杂信号检测和分析的多功能传感系统方面,仍然存在一个关键挑战。在这里,我们通过激光诱导石墨化开发了一种结合机器学习的柔性传感器,用于实时触觉感应和语音识别。具有摩擦电层的智能传感器可以通过接触带电效应将局部压力转换为电信号,而无需外部偏置,当暴露于各种机械刺激时,它具有特征响应行为。通过特殊的图案设计,构建了由数字阵列触摸面板组成的智能人机交互控制系统,以控制电子设备。基于机器学习,可以高精度地实现语音变化的实时监测和识别。受机器学习赋能的柔性传感器为开发灵活触觉感应、实时健康监测、人机交互和智能可穿戴设备提供了有前景的平台。