  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.
