In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human motion energy was collected by human body arrangement, and human motion posture was monitored according to the changes of output electrical signals. In 2012, Academician Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals, so it can be used as a self-driven sensor. Teng-based sensors have the advantages of simple structure and high instantaneous power density, which provides an important means for building intelligent sensor systems. At the same time, machine learning, as a technology with low cost, short development cycle, strong data processing ability and prediction ability, has a significant effect on the processing of a large number of electrical signals generated by TENG, and the combination with TENG sensors will promote the rapid development of intelligent sensor networks in the future. Therefore, this paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability, and aims to evaluate the feasibility of the sound perception module architecture in ubiquitous sensor networks.

翻译：在物联网时代，如何开发具有可持续供电、易于部署与灵活使用的智能传感器系统已成为亟待解决的难题。传统供电方式在使用中需频繁更换或充电，限制了可穿戴设备的发展。利用聚四氟乙烯（PTFE）与铝（Al）箔制备了接触-分离式摩擦纳米发电机（TENG），通过人体布置采集人体运动能量，并依据输出电信号变化监测人体运动姿态。2012年，王中林院士团队发明了摩擦纳米发电机（TENG），其以麦克斯韦位移电流为驱动力，将机械刺激直接转换为电信号，因此可用作自驱动传感器。基于TENG的传感器具有结构简单、瞬时功率密度高等优势，为构建智能传感器系统提供了重要手段。同时，机器学习作为一种成本低、开发周期短、数据处理与预测能力强的技术，对处理TENG产生的大量电信号具有显著效果，与TENG传感器的结合将推动未来智能传感器网络的快速发展。因此，本文基于TENG的智能声音监测与识别系统，该系统具备良好的声音识别能力，旨在评估声音感知模块架构在泛在传感器网络中的可行性。