Intelligent System Application Research Combining TENG-Based Self-Powered Sensor With Deep Learning Technique

In the era of The Internet of Things,how to develop a smart sensor system with sustainable power supply,easy deployment and flexible use has become an urgent problem to be solved.In2012,Zhonglin Wang and his team invented the triboelectric nanogenerator(TENG),which can directly convert mechanical motion into electrical signals by Maxwell’s Displacement Current as the driving force,thus it can be used as a self-driven sensor.Sensors based on TENG have the advantages of simple structure and high instantaneous power density,which provide an important means to build intelligent sensor systems.Meanwhile,machine learning,as a technique with low cost,short development cycle,and strong data processing capabilities and predictive capabilities,is effective in processing the large amount of electrical signals generated by TENG,combining with TENG sensors will promote the rapid development of intelligent sensor networks in the future.Chapter 2 introduces a TENG-based intelligent sound monitoring and recognition system with excellent sound recognition capability,aiming to assess the feasibility of sound-aware module architecture in ubiquitous sensor networks.Urban sound management is required in a variety of fields such as transportation,security,water conservancy and construction,among others.Given the diverse array of available noise sensors and the widespread opportunity to connect these sensors via mobile broadband Internet access,many researchers are eager to apply sound-sensor networks for urban sound management.Existing sensing networks typically consist of expensive informationsensing devices,the cost and maintenance of which limit their large-scale,ubiquitous deployment,thus narrowing their functional measurement range.Herein,an innovative,low-cost,sound-driven triboelectric nanogenerator(SDTENG)-based self-powered sensor is proposed,from which the SDTENG is primarily comprised of fluorinated ethylene propylene membranes,conductive fabrics,acrylic shells,and Kapton spacers.The SDTENG-based sensor has been integrated with a deep learning technique in the present study to construct an intelligent sound monitoring and identification system,which is capable of recognizing a suite of common road and traffic sounds with high classification accuracies of 99% in most cases.The novel SDTENG-based self-powered sensor combined with deep learning technique demonstrates a tremendous application potential in urban sound management,which will show the excellent application prospects in the field of ubiquitous sensor networks.In Chapter 3,we investigate a TENG-based intelligent sensing system and heart rate monitoring system,aiming to help the elderly or people with special diseases to monitor and understand their health status at any time and provide help to alleviate social medical resources.Accurate monitoring of respiratory status is critical for human health assessment,especially for a healthy life,early diagnosis of diseases and medical care.Most current respiratory monitoring devices,such as respiratory monitors,are inconvenient for continuous biomonitoring in a wearable way at home.With a range of compelling features,such as light weight and high sensitivity,TENG have become an emerging and cost-effective biotechnology for long-term and continuous respiratory monitoring in a wearable manner.Here,an intelligent sensing system(RSMS)supporting respiratory state monitoring with self-powered,low-cost and flexible triboelectric sensors and a well-trained convolutional neural network model based on prototype learning is presented.The RSMS is capable of recognizing a suite of common respiratory states with a classification accuracy of up to 98.3% in most cases.The TENG-based RSMS is important for healthy growth of young and special patient populations,as well as for health monitoring and medical care of elderly and rehabilitating patients.This work aims to broaden the pathway for remote human respiratory status analysis and providing a diverse perspective for real-time and longterm health monitoring.