PEO-PDMS-based triboelectric nanogenerators as self-powered sensors for driver status monitoring

被引:44
作者
Luo, Fangyuan [1 ]
Chen, Bin [1 ]
Ran, Xu [1 ]
Ouyang, Wei [1 ]
Shang, Liang [2 ,3 ]
机构
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuit & Intelligent, Chongqing 400715, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China
[3] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110016, Peoples R China
关键词
PEO-PDMS-based triboelectric nanogenerator; Self -powered sensor; Respiratory signal detection; Driver status monitoring; DROWSINESS DETECTION; PRESSURE SENSORS; POLARIZATION; CHALLENGES; FLUORIDE; SYSTEM; SAFETY; TEA;
D O I
10.1016/j.cej.2022.138961
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The number of traffic accidents is growing with the ever-increasing vehicles, and most of the traffic accidents are due to fatigue or distracted driving. Thus, it is necessary to monitor the driver's status in real-time. However, current detection methods mainly rely on sensors that need a continuous power supply. Here, a polyethylene oxide (PEO)-polydimethylsiloxane (PDMS)-based triboelectric nanogenerator as a self-powered sensor was designed for biological signal monitoring. According to the experimental results, by adding green and low-cost substances sodium chloride and tea powder in PEO and PDMS films, respectively, the output voltage and current of the sensor are increased by about 9.75 and 8.21 times. The proposed sensor has a high sensitivity of 0.7 V/kPa in the linear range of 0-50 kPa and a fast response time of 36 ms. Moreover, a high-performance and low-cost driver status monitoring system was designed. The sensors were attached to the driver's neck and seat belt to monitor neck activities and breathing states. By extracting parameters from the output voltage waveform, the system can judge and display the degrees of driver fatigue and concentration. The proposed system is conducive to improving road traffic safety and has the potential to be applied in intelligent transportation.
引用
收藏
页数:14
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